From a0b4acbce7dbee0d961efcac9d40be6f651b2400 Mon Sep 17 00:00:00 2001
From:  <>
Date: Sat, 16 Jul 2022 13:28:56 +0000
Subject: [PATCH] Deployed 50531d6 with MkDocs version: 1.3.0

---
 examples/simple-mag/confusion-matrix.png      | Bin 58514 -> 0 bytes
 examples/simple-mag/mag-confusion.png         | Bin 0 -> 1572115 bytes
 examples/simple-mag/train/index.html          | 214 +++---
 examples/simple-mag/train/train.ipynb         | 257 +++----
 examples/simple/deploy/deploy.ipynb           |  65 +-
 examples/simple/deploy/index.html             |  72 +-
 examples/simple/ss-confusion.png              | Bin 0 -> 2870960 bytes
 examples/simple/train/index.html              | 508 +++++++++++++-
 examples/simple/train/train.ipynb             | 657 +++++++++++++++++-
 search/search_index.json                      |   2 +-
 sitemap.xml.gz                                | Bin 214 -> 214 bytes
 thesis/chapters/0_abstract.tex                |   4 +-
 thesis/chapters/1_introduction.tex            |  44 +-
 thesis/chapters/2_background.tex              |  50 +-
 thesis/chapters/3_methods.tex                 |  26 +-
 thesis/chapters/4_design.tex                  |  26 +-
 thesis/chapters/5_case/2-stage.tex            |   4 +-
 thesis/chapters/5_case/features.tex           |  29 +-
 thesis/chapters/5_case/introduction.tex       |   8 +-
 thesis/chapters/5_case/naive-bayes.tex        |  42 +-
 thesis/chapters/7_conclusion.tex              |   2 +
 thesis/figures/confusion-matrix.png           | Bin 58514 -> 0 bytes
 .../{UL_PMS-kleur.eps => leiden-logo.eps}     | Bin
 thesis/figures/mag-confusion.png              | Bin 0 -> 1572115 bytes
 thesis/figures/mag-distribution.png           | Bin 29986 -> 212503 bytes
 thesis/figures/ss-confusion.png               | Bin 164208 -> 2842544 bytes
 thesis/figures/ss-distribution.png            | Bin 65683 -> 810837 bytes
 thesis/frontpage/frontpage.tex                |   2 +-
 thesis/llncs.cls                              |   7 +-
 thesis/main.tex                               |   5 +
 thesis/ref.bib                                |  62 ++
 31 files changed, 1634 insertions(+), 452 deletions(-)
 delete mode 100644 examples/simple-mag/confusion-matrix.png
 create mode 100644 examples/simple-mag/mag-confusion.png
 create mode 100644 examples/simple/ss-confusion.png
 delete mode 100644 thesis/figures/confusion-matrix.png
 rename thesis/figures/{UL_PMS-kleur.eps => leiden-logo.eps} (100%)
 create mode 100644 thesis/figures/mag-confusion.png

diff --git a/examples/simple-mag/confusion-matrix.png b/examples/simple-mag/confusion-matrix.png
deleted file mode 100644
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diff --git a/examples/simple-mag/train/index.html b/examples/simple-mag/train/index.html
index 65bb54d..2f0c6bd 100644
--- a/examples/simple-mag/train/index.html
+++ b/examples/simple-mag/train/index.html
@@ -1868,8 +1868,8 @@ y_test = [d[1] for d in test_data]</div>
 
 
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
-<pre>100%|██████████| 84000/84000 [00:08&lt;00:00, 9514.74it/s] 
-100%|██████████| 22399/22399 [00:02&lt;00:00, 7728.92it/s] 
+<pre>100%|██████████| 84000/84000 [00:16&lt;00:00, 4964.08it/s]
+100%|██████████| 22399/22399 [00:05&lt;00:00, 3847.04it/s]
 </pre>
 </div>
 </div>
@@ -2163,10 +2163,10 @@ results.sort_values("rank_test_score")</div>
   <tbody>
     <tr>
       <th>12</th>
-      <td>1.410234</td>
-      <td>0.165616</td>
-      <td>0.616949</td>
-      <td>0.017791</td>
+      <td>2.352721</td>
+      <td>0.257936</td>
+      <td>1.357399</td>
+      <td>0.094580</td>
       <td>1</td>
       <td>True</td>
       <td>0.05</td>
@@ -2181,10 +2181,10 @@ results.sort_values("rank_test_score")</div>
     </tr>
     <tr>
       <th>15</th>
-      <td>1.356129</td>
-      <td>0.033539</td>
-      <td>0.799676</td>
-      <td>0.077166</td>
+      <td>2.544958</td>
+      <td>0.051976</td>
+      <td>1.245658</td>
+      <td>0.137108</td>
       <td>1</td>
       <td>True</td>
       <td>0.1</td>
@@ -2199,10 +2199,10 @@ results.sort_values("rank_test_score")</div>
     </tr>
     <tr>
       <th>18</th>
-      <td>1.191132</td>
-      <td>0.046271</td>
-      <td>0.633976</td>
-      <td>0.009344</td>
+      <td>2.472888</td>
+      <td>0.053978</td>
+      <td>1.447181</td>
+      <td>0.051170</td>
       <td>1</td>
       <td>False</td>
       <td>0.05</td>
@@ -2217,10 +2217,10 @@ results.sort_values("rank_test_score")</div>
     </tr>
     <tr>
       <th>21</th>
-      <td>1.206345</td>
-      <td>0.121633</td>
-      <td>0.494508</td>
-      <td>0.101456</td>
+      <td>2.467637</td>
+      <td>0.005358</td>
+      <td>1.216254</td>
+      <td>0.048319</td>
       <td>1</td>
       <td>False</td>
       <td>0.1</td>
@@ -2235,10 +2235,10 @@ results.sort_values("rank_test_score")</div>
     </tr>
     <tr>
       <th>3</th>
-      <td>1.434749</td>
-      <td>0.077467</td>
-      <td>0.665522</td>
-      <td>0.042225</td>
+      <td>2.477612</td>
+      <td>0.134880</td>
+      <td>1.428521</td>
+      <td>0.017417</td>
       <td>0.5</td>
       <td>True</td>
       <td>0.1</td>
@@ -2253,10 +2253,10 @@ results.sort_values("rank_test_score")</div>
     </tr>
     <tr>
       <th>0</th>
-      <td>1.338873</td>
-      <td>0.100248</td>
-      <td>0.791040</td>
-      <td>0.061094</td>
+      <td>2.484810</td>
+      <td>0.067836</td>
+      <td>1.249979</td>
+      <td>0.209959</td>
       <td>0.5</td>
       <td>True</td>
       <td>0.05</td>
@@ -2271,10 +2271,10 @@ results.sort_values("rank_test_score")</div>
     </tr>
     <tr>
       <th>9</th>
-      <td>1.429841</td>
-      <td>0.071771</td>
-      <td>0.868423</td>
-      <td>0.097744</td>
+      <td>2.452453</td>
+      <td>0.049376</td>
+      <td>1.279694</td>
+      <td>0.089200</td>
       <td>0.5</td>
       <td>False</td>
       <td>0.1</td>
@@ -2289,10 +2289,10 @@ results.sort_values("rank_test_score")</div>
     </tr>
     <tr>
       <th>6</th>
-      <td>1.320994</td>
-      <td>0.115772</td>
-      <td>0.708544</td>
-      <td>0.028758</td>
+      <td>2.748628</td>
+      <td>0.098077</td>
+      <td>1.255213</td>
+      <td>0.101334</td>
       <td>0.5</td>
       <td>False</td>
       <td>0.05</td>
@@ -2307,10 +2307,10 @@ results.sort_values("rank_test_score")</div>
     </tr>
     <tr>
       <th>13</th>
-      <td>1.430184</td>
-      <td>0.068179</td>
-      <td>0.667534</td>
-      <td>0.045122</td>
+      <td>2.371907</td>
+      <td>0.204820</td>
+      <td>1.375143</td>
+      <td>0.193984</td>
       <td>1</td>
       <td>True</td>
       <td>0.05</td>
@@ -2325,10 +2325,10 @@ results.sort_values("rank_test_score")</div>
     </tr>
     <tr>
       <th>16</th>
-      <td>1.230683</td>
-      <td>0.097298</td>
-      <td>0.657511</td>
-      <td>0.015534</td>
+      <td>2.557486</td>
+      <td>0.184332</td>
+      <td>1.272137</td>
+      <td>0.133980</td>
       <td>1</td>
       <td>True</td>
       <td>0.1</td>
@@ -2343,10 +2343,10 @@ results.sort_values("rank_test_score")</div>
     </tr>
     <tr>
       <th>4</th>
-      <td>1.247198</td>
-      <td>0.061587</td>
-      <td>0.684918</td>
-      <td>0.085735</td>
+      <td>2.362093</td>
+      <td>0.109796</td>
+      <td>1.344960</td>
+      <td>0.083562</td>
       <td>0.5</td>
       <td>True</td>
       <td>0.1</td>
@@ -2361,10 +2361,10 @@ results.sort_values("rank_test_score")</div>
     </tr>
     <tr>
       <th>1</th>
-      <td>1.367997</td>
-      <td>0.161019</td>
-      <td>0.720563</td>
-      <td>0.047895</td>
+      <td>2.668012</td>
+      <td>0.131751</td>
+      <td>1.383441</td>
+      <td>0.049157</td>
       <td>0.5</td>
       <td>True</td>
       <td>0.05</td>
@@ -2379,10 +2379,10 @@ results.sort_values("rank_test_score")</div>
     </tr>
     <tr>
       <th>19</th>
-      <td>1.276051</td>
-      <td>0.053059</td>
-      <td>0.670412</td>
-      <td>0.055486</td>
+      <td>2.517576</td>
+      <td>0.154192</td>
+      <td>1.374269</td>
+      <td>0.026773</td>
       <td>1</td>
       <td>False</td>
       <td>0.05</td>
@@ -2397,10 +2397,10 @@ results.sort_values("rank_test_score")</div>
     </tr>
     <tr>
       <th>22</th>
-      <td>1.098513</td>
-      <td>0.062989</td>
-      <td>0.454028</td>
-      <td>0.049762</td>
+      <td>2.386240</td>
+      <td>0.069718</td>
+      <td>0.967638</td>
+      <td>0.076766</td>
       <td>1</td>
       <td>False</td>
       <td>0.1</td>
@@ -2415,10 +2415,10 @@ results.sort_values("rank_test_score")</div>
     </tr>
     <tr>
       <th>7</th>
-      <td>1.246671</td>
-      <td>0.070173</td>
-      <td>0.678239</td>
-      <td>0.061924</td>
+      <td>2.416211</td>
+      <td>0.138001</td>
+      <td>1.316785</td>
+      <td>0.036402</td>
       <td>0.5</td>
       <td>False</td>
       <td>0.05</td>
@@ -2433,10 +2433,10 @@ results.sort_values("rank_test_score")</div>
     </tr>
     <tr>
       <th>10</th>
-      <td>1.335585</td>
-      <td>0.055454</td>
-      <td>0.714089</td>
-      <td>0.031715</td>
+      <td>2.397768</td>
+      <td>0.244951</td>
+      <td>1.330007</td>
+      <td>0.008983</td>
       <td>0.5</td>
       <td>False</td>
       <td>0.1</td>
@@ -2451,10 +2451,10 @@ results.sort_values("rank_test_score")</div>
     </tr>
     <tr>
       <th>14</th>
-      <td>1.433829</td>
-      <td>0.102381</td>
-      <td>0.584196</td>
-      <td>0.020291</td>
+      <td>2.590127</td>
+      <td>0.054396</td>
+      <td>1.155976</td>
+      <td>0.187602</td>
       <td>1</td>
       <td>True</td>
       <td>0.05</td>
@@ -2469,10 +2469,10 @@ results.sort_values("rank_test_score")</div>
     </tr>
     <tr>
       <th>17</th>
-      <td>1.259510</td>
-      <td>0.128875</td>
-      <td>0.561018</td>
-      <td>0.006103</td>
+      <td>2.497556</td>
+      <td>0.085952</td>
+      <td>1.169808</td>
+      <td>0.061336</td>
       <td>1</td>
       <td>True</td>
       <td>0.1</td>
@@ -2487,10 +2487,10 @@ results.sort_values("rank_test_score")</div>
     </tr>
     <tr>
       <th>2</th>
-      <td>1.369987</td>
-      <td>0.103349</td>
-      <td>0.699614</td>
-      <td>0.033642</td>
+      <td>2.467611</td>
+      <td>0.168399</td>
+      <td>1.313936</td>
+      <td>0.109695</td>
       <td>0.5</td>
       <td>True</td>
       <td>0.05</td>
@@ -2505,10 +2505,10 @@ results.sort_values("rank_test_score")</div>
     </tr>
     <tr>
       <th>5</th>
-      <td>1.292009</td>
-      <td>0.083116</td>
-      <td>0.621565</td>
-      <td>0.058068</td>
+      <td>2.339322</td>
+      <td>0.092595</td>
+      <td>1.209515</td>
+      <td>0.132825</td>
       <td>0.5</td>
       <td>True</td>
       <td>0.1</td>
@@ -2523,10 +2523,10 @@ results.sort_values("rank_test_score")</div>
     </tr>
     <tr>
       <th>20</th>
-      <td>1.158813</td>
-      <td>0.039281</td>
-      <td>0.524616</td>
-      <td>0.016054</td>
+      <td>2.644488</td>
+      <td>0.119967</td>
+      <td>1.238554</td>
+      <td>0.048871</td>
       <td>1</td>
       <td>False</td>
       <td>0.05</td>
@@ -2541,10 +2541,10 @@ results.sort_values("rank_test_score")</div>
     </tr>
     <tr>
       <th>8</th>
-      <td>1.302560</td>
-      <td>0.095545</td>
-      <td>0.709228</td>
-      <td>0.144463</td>
+      <td>2.456030</td>
+      <td>0.072136</td>
+      <td>1.240463</td>
+      <td>0.092383</td>
       <td>0.5</td>
       <td>False</td>
       <td>0.05</td>
@@ -2559,10 +2559,10 @@ results.sort_values("rank_test_score")</div>
     </tr>
     <tr>
       <th>23</th>
-      <td>1.093018</td>
-      <td>0.033991</td>
-      <td>0.331076</td>
-      <td>0.005935</td>
+      <td>2.335796</td>
+      <td>0.082338</td>
+      <td>0.786187</td>
+      <td>0.073499</td>
       <td>1</td>
       <td>False</td>
       <td>0.1</td>
@@ -2577,10 +2577,10 @@ results.sort_values("rank_test_score")</div>
     </tr>
     <tr>
       <th>11</th>
-      <td>1.534622</td>
-      <td>0.081777</td>
-      <td>0.649834</td>
-      <td>0.033149</td>
+      <td>2.530331</td>
+      <td>0.107199</td>
+      <td>1.247842</td>
+      <td>0.078229</td>
       <td>0.5</td>
       <td>False</td>
       <td>0.1</td>
@@ -2683,7 +2683,7 @@ classifier.fit(X_train, y_train)</div>
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[12]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
         
         
         <div class="CodeMirror cm-s-jupyter">
@@ -2704,9 +2704,9 @@ classifier.fit(X_train, y_train)</div>
 <span class="n">y_predicted</span> <span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
 
 <span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">&quot;figure.figsize&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">15</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">&quot;figure.figsize&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">18</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">&quot;figure.facecolor&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;white&quot;</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">&quot;font.size&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">12</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">&quot;font.size&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">22</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">&quot;axes.xmargin&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
 
 <span class="nb">print</span><span class="p">(</span><span class="n">metrics</span><span class="o">.</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_predicted</span><span class="p">,</span> <span class="n">digits</span><span class="o">=</span><span class="mi">4</span><span class="p">))</span>
@@ -2717,7 +2717,8 @@ classifier.fit(X_train, y_train)</div>
     <span class="n">normalize</span><span class="o">=</span><span class="s2">&quot;pred&quot;</span><span class="p">,</span>
     <span class="n">values_format</span><span class="o">=</span><span class="s2">&quot;.2f&quot;</span><span class="p">,</span>
 <span class="p">)</span>
-<span class="kc">None</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s2">&quot;mag-confusion.png&quot;</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">600</span><span class="p">)</span>
 </pre></div>
 <div id="cell-6" class="clipboard-copy-txt">from sklearn import metrics
 import matplotlib.pyplot as plt
@@ -2725,9 +2726,9 @@ import matplotlib.pyplot as plt
 y_predicted = classifier.predict(X_test)
 
 %matplotlib inline
-plt.rcParams["figure.figsize"] = (30, 15)
+plt.rcParams["figure.figsize"] = (20, 18)
 plt.rcParams["figure.facecolor"] = "white"
-plt.rcParams["font.size"] = 12
+plt.rcParams["font.size"] = 22
 plt.rcParams["axes.xmargin"] = 0
 
 print(metrics.classification_report(y_test, y_predicted, digits=4))
@@ -2738,7 +2739,8 @@ metrics.ConfusionMatrixDisplay.from_predictions(
     normalize="pred",
     values_format=".2f",
 )
-None</div>
+plt.tight_layout()
+plt.savefig("mag-confusion.png", dpi=600)</div>
     
         </div>
     </div>
@@ -2787,7 +2789,7 @@ weighted avg     0.6817    0.6803    0.6796     22399
 
 
 <div class="jp-RenderedImage jp-OutputArea-output ">
-<img 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"
 class="
 "
 >
diff --git a/examples/simple-mag/train/train.ipynb b/examples/simple-mag/train/train.ipynb
index 75ed417..09faf2c 100644
--- a/examples/simple-mag/train/train.ipynb
+++ b/examples/simple-mag/train/train.ipynb
@@ -19,8 +19,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 84000/84000 [00:08<00:00, 9514.74it/s] \n",
-      "100%|██████████| 22399/22399 [00:02<00:00, 7728.92it/s] \n"
+      "100%|██████████| 84000/84000 [00:16<00:00, 4964.08it/s]\n",
+      "100%|██████████| 22399/22399 [00:05<00:00, 3847.04it/s]\n"
      ]
     }
    ],
@@ -162,10 +162,10 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
-       "      <td>1.410234</td>\n",
-       "      <td>0.165616</td>\n",
-       "      <td>0.616949</td>\n",
-       "      <td>0.017791</td>\n",
+       "      <td>2.352721</td>\n",
+       "      <td>0.257936</td>\n",
+       "      <td>1.357399</td>\n",
+       "      <td>0.094580</td>\n",
        "      <td>1</td>\n",
        "      <td>True</td>\n",
        "      <td>0.05</td>\n",
@@ -180,10 +180,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
-       "      <td>1.356129</td>\n",
-       "      <td>0.033539</td>\n",
-       "      <td>0.799676</td>\n",
-       "      <td>0.077166</td>\n",
+       "      <td>2.544958</td>\n",
+       "      <td>0.051976</td>\n",
+       "      <td>1.245658</td>\n",
+       "      <td>0.137108</td>\n",
        "      <td>1</td>\n",
        "      <td>True</td>\n",
        "      <td>0.1</td>\n",
@@ -198,10 +198,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
-       "      <td>1.191132</td>\n",
-       "      <td>0.046271</td>\n",
-       "      <td>0.633976</td>\n",
-       "      <td>0.009344</td>\n",
+       "      <td>2.472888</td>\n",
+       "      <td>0.053978</td>\n",
+       "      <td>1.447181</td>\n",
+       "      <td>0.051170</td>\n",
        "      <td>1</td>\n",
        "      <td>False</td>\n",
        "      <td>0.05</td>\n",
@@ -216,10 +216,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
-       "      <td>1.206345</td>\n",
-       "      <td>0.121633</td>\n",
-       "      <td>0.494508</td>\n",
-       "      <td>0.101456</td>\n",
+       "      <td>2.467637</td>\n",
+       "      <td>0.005358</td>\n",
+       "      <td>1.216254</td>\n",
+       "      <td>0.048319</td>\n",
        "      <td>1</td>\n",
        "      <td>False</td>\n",
        "      <td>0.1</td>\n",
@@ -234,10 +234,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>1.434749</td>\n",
-       "      <td>0.077467</td>\n",
-       "      <td>0.665522</td>\n",
-       "      <td>0.042225</td>\n",
+       "      <td>2.477612</td>\n",
+       "      <td>0.134880</td>\n",
+       "      <td>1.428521</td>\n",
+       "      <td>0.017417</td>\n",
        "      <td>0.5</td>\n",
        "      <td>True</td>\n",
        "      <td>0.1</td>\n",
@@ -252,10 +252,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1.338873</td>\n",
-       "      <td>0.100248</td>\n",
-       "      <td>0.791040</td>\n",
-       "      <td>0.061094</td>\n",
+       "      <td>2.484810</td>\n",
+       "      <td>0.067836</td>\n",
+       "      <td>1.249979</td>\n",
+       "      <td>0.209959</td>\n",
        "      <td>0.5</td>\n",
        "      <td>True</td>\n",
        "      <td>0.05</td>\n",
@@ -270,10 +270,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>1.429841</td>\n",
-       "      <td>0.071771</td>\n",
-       "      <td>0.868423</td>\n",
-       "      <td>0.097744</td>\n",
+       "      <td>2.452453</td>\n",
+       "      <td>0.049376</td>\n",
+       "      <td>1.279694</td>\n",
+       "      <td>0.089200</td>\n",
        "      <td>0.5</td>\n",
        "      <td>False</td>\n",
        "      <td>0.1</td>\n",
@@ -288,10 +288,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>1.320994</td>\n",
-       "      <td>0.115772</td>\n",
-       "      <td>0.708544</td>\n",
-       "      <td>0.028758</td>\n",
+       "      <td>2.748628</td>\n",
+       "      <td>0.098077</td>\n",
+       "      <td>1.255213</td>\n",
+       "      <td>0.101334</td>\n",
        "      <td>0.5</td>\n",
        "      <td>False</td>\n",
        "      <td>0.05</td>\n",
@@ -306,10 +306,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
-       "      <td>1.430184</td>\n",
-       "      <td>0.068179</td>\n",
-       "      <td>0.667534</td>\n",
-       "      <td>0.045122</td>\n",
+       "      <td>2.371907</td>\n",
+       "      <td>0.204820</td>\n",
+       "      <td>1.375143</td>\n",
+       "      <td>0.193984</td>\n",
        "      <td>1</td>\n",
        "      <td>True</td>\n",
        "      <td>0.05</td>\n",
@@ -324,10 +324,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
-       "      <td>1.230683</td>\n",
-       "      <td>0.097298</td>\n",
-       "      <td>0.657511</td>\n",
-       "      <td>0.015534</td>\n",
+       "      <td>2.557486</td>\n",
+       "      <td>0.184332</td>\n",
+       "      <td>1.272137</td>\n",
+       "      <td>0.133980</td>\n",
        "      <td>1</td>\n",
        "      <td>True</td>\n",
        "      <td>0.1</td>\n",
@@ -342,10 +342,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>1.247198</td>\n",
-       "      <td>0.061587</td>\n",
-       "      <td>0.684918</td>\n",
-       "      <td>0.085735</td>\n",
+       "      <td>2.362093</td>\n",
+       "      <td>0.109796</td>\n",
+       "      <td>1.344960</td>\n",
+       "      <td>0.083562</td>\n",
        "      <td>0.5</td>\n",
        "      <td>True</td>\n",
        "      <td>0.1</td>\n",
@@ -360,10 +360,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1.367997</td>\n",
-       "      <td>0.161019</td>\n",
-       "      <td>0.720563</td>\n",
-       "      <td>0.047895</td>\n",
+       "      <td>2.668012</td>\n",
+       "      <td>0.131751</td>\n",
+       "      <td>1.383441</td>\n",
+       "      <td>0.049157</td>\n",
        "      <td>0.5</td>\n",
        "      <td>True</td>\n",
        "      <td>0.05</td>\n",
@@ -378,10 +378,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
-       "      <td>1.276051</td>\n",
-       "      <td>0.053059</td>\n",
-       "      <td>0.670412</td>\n",
-       "      <td>0.055486</td>\n",
+       "      <td>2.517576</td>\n",
+       "      <td>0.154192</td>\n",
+       "      <td>1.374269</td>\n",
+       "      <td>0.026773</td>\n",
        "      <td>1</td>\n",
        "      <td>False</td>\n",
        "      <td>0.05</td>\n",
@@ -396,10 +396,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
-       "      <td>1.098513</td>\n",
-       "      <td>0.062989</td>\n",
-       "      <td>0.454028</td>\n",
-       "      <td>0.049762</td>\n",
+       "      <td>2.386240</td>\n",
+       "      <td>0.069718</td>\n",
+       "      <td>0.967638</td>\n",
+       "      <td>0.076766</td>\n",
        "      <td>1</td>\n",
        "      <td>False</td>\n",
        "      <td>0.1</td>\n",
@@ -414,10 +414,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>1.246671</td>\n",
-       "      <td>0.070173</td>\n",
-       "      <td>0.678239</td>\n",
-       "      <td>0.061924</td>\n",
+       "      <td>2.416211</td>\n",
+       "      <td>0.138001</td>\n",
+       "      <td>1.316785</td>\n",
+       "      <td>0.036402</td>\n",
        "      <td>0.5</td>\n",
        "      <td>False</td>\n",
        "      <td>0.05</td>\n",
@@ -432,10 +432,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
-       "      <td>1.335585</td>\n",
-       "      <td>0.055454</td>\n",
-       "      <td>0.714089</td>\n",
-       "      <td>0.031715</td>\n",
+       "      <td>2.397768</td>\n",
+       "      <td>0.244951</td>\n",
+       "      <td>1.330007</td>\n",
+       "      <td>0.008983</td>\n",
        "      <td>0.5</td>\n",
        "      <td>False</td>\n",
        "      <td>0.1</td>\n",
@@ -450,10 +450,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
-       "      <td>1.433829</td>\n",
-       "      <td>0.102381</td>\n",
-       "      <td>0.584196</td>\n",
-       "      <td>0.020291</td>\n",
+       "      <td>2.590127</td>\n",
+       "      <td>0.054396</td>\n",
+       "      <td>1.155976</td>\n",
+       "      <td>0.187602</td>\n",
        "      <td>1</td>\n",
        "      <td>True</td>\n",
        "      <td>0.05</td>\n",
@@ -468,10 +468,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
-       "      <td>1.259510</td>\n",
-       "      <td>0.128875</td>\n",
-       "      <td>0.561018</td>\n",
-       "      <td>0.006103</td>\n",
+       "      <td>2.497556</td>\n",
+       "      <td>0.085952</td>\n",
+       "      <td>1.169808</td>\n",
+       "      <td>0.061336</td>\n",
        "      <td>1</td>\n",
        "      <td>True</td>\n",
        "      <td>0.1</td>\n",
@@ -486,10 +486,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>1.369987</td>\n",
-       "      <td>0.103349</td>\n",
-       "      <td>0.699614</td>\n",
-       "      <td>0.033642</td>\n",
+       "      <td>2.467611</td>\n",
+       "      <td>0.168399</td>\n",
+       "      <td>1.313936</td>\n",
+       "      <td>0.109695</td>\n",
        "      <td>0.5</td>\n",
        "      <td>True</td>\n",
        "      <td>0.05</td>\n",
@@ -504,10 +504,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>1.292009</td>\n",
-       "      <td>0.083116</td>\n",
-       "      <td>0.621565</td>\n",
-       "      <td>0.058068</td>\n",
+       "      <td>2.339322</td>\n",
+       "      <td>0.092595</td>\n",
+       "      <td>1.209515</td>\n",
+       "      <td>0.132825</td>\n",
        "      <td>0.5</td>\n",
        "      <td>True</td>\n",
        "      <td>0.1</td>\n",
@@ -522,10 +522,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
-       "      <td>1.158813</td>\n",
-       "      <td>0.039281</td>\n",
-       "      <td>0.524616</td>\n",
-       "      <td>0.016054</td>\n",
+       "      <td>2.644488</td>\n",
+       "      <td>0.119967</td>\n",
+       "      <td>1.238554</td>\n",
+       "      <td>0.048871</td>\n",
        "      <td>1</td>\n",
        "      <td>False</td>\n",
        "      <td>0.05</td>\n",
@@ -540,10 +540,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>1.302560</td>\n",
-       "      <td>0.095545</td>\n",
-       "      <td>0.709228</td>\n",
-       "      <td>0.144463</td>\n",
+       "      <td>2.456030</td>\n",
+       "      <td>0.072136</td>\n",
+       "      <td>1.240463</td>\n",
+       "      <td>0.092383</td>\n",
        "      <td>0.5</td>\n",
        "      <td>False</td>\n",
        "      <td>0.05</td>\n",
@@ -558,10 +558,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
-       "      <td>1.093018</td>\n",
-       "      <td>0.033991</td>\n",
-       "      <td>0.331076</td>\n",
-       "      <td>0.005935</td>\n",
+       "      <td>2.335796</td>\n",
+       "      <td>0.082338</td>\n",
+       "      <td>0.786187</td>\n",
+       "      <td>0.073499</td>\n",
        "      <td>1</td>\n",
        "      <td>False</td>\n",
        "      <td>0.1</td>\n",
@@ -576,10 +576,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
-       "      <td>1.534622</td>\n",
-       "      <td>0.081777</td>\n",
-       "      <td>0.649834</td>\n",
-       "      <td>0.033149</td>\n",
+       "      <td>2.530331</td>\n",
+       "      <td>0.107199</td>\n",
+       "      <td>1.247842</td>\n",
+       "      <td>0.078229</td>\n",
        "      <td>0.5</td>\n",
        "      <td>False</td>\n",
        "      <td>0.1</td>\n",
@@ -598,30 +598,30 @@
       ],
       "text/plain": [
        "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
-       "12       1.410234      0.165616         0.616949        0.017791   \n",
-       "15       1.356129      0.033539         0.799676        0.077166   \n",
-       "18       1.191132      0.046271         0.633976        0.009344   \n",
-       "21       1.206345      0.121633         0.494508        0.101456   \n",
-       "3        1.434749      0.077467         0.665522        0.042225   \n",
-       "0        1.338873      0.100248         0.791040        0.061094   \n",
-       "9        1.429841      0.071771         0.868423        0.097744   \n",
-       "6        1.320994      0.115772         0.708544        0.028758   \n",
-       "13       1.430184      0.068179         0.667534        0.045122   \n",
-       "16       1.230683      0.097298         0.657511        0.015534   \n",
-       "4        1.247198      0.061587         0.684918        0.085735   \n",
-       "1        1.367997      0.161019         0.720563        0.047895   \n",
-       "19       1.276051      0.053059         0.670412        0.055486   \n",
-       "22       1.098513      0.062989         0.454028        0.049762   \n",
-       "7        1.246671      0.070173         0.678239        0.061924   \n",
-       "10       1.335585      0.055454         0.714089        0.031715   \n",
-       "14       1.433829      0.102381         0.584196        0.020291   \n",
-       "17       1.259510      0.128875         0.561018        0.006103   \n",
-       "2        1.369987      0.103349         0.699614        0.033642   \n",
-       "5        1.292009      0.083116         0.621565        0.058068   \n",
-       "20       1.158813      0.039281         0.524616        0.016054   \n",
-       "8        1.302560      0.095545         0.709228        0.144463   \n",
-       "23       1.093018      0.033991         0.331076        0.005935   \n",
-       "11       1.534622      0.081777         0.649834        0.033149   \n",
+       "12       2.352721      0.257936         1.357399        0.094580   \n",
+       "15       2.544958      0.051976         1.245658        0.137108   \n",
+       "18       2.472888      0.053978         1.447181        0.051170   \n",
+       "21       2.467637      0.005358         1.216254        0.048319   \n",
+       "3        2.477612      0.134880         1.428521        0.017417   \n",
+       "0        2.484810      0.067836         1.249979        0.209959   \n",
+       "9        2.452453      0.049376         1.279694        0.089200   \n",
+       "6        2.748628      0.098077         1.255213        0.101334   \n",
+       "13       2.371907      0.204820         1.375143        0.193984   \n",
+       "16       2.557486      0.184332         1.272137        0.133980   \n",
+       "4        2.362093      0.109796         1.344960        0.083562   \n",
+       "1        2.668012      0.131751         1.383441        0.049157   \n",
+       "19       2.517576      0.154192         1.374269        0.026773   \n",
+       "22       2.386240      0.069718         0.967638        0.076766   \n",
+       "7        2.416211      0.138001         1.316785        0.036402   \n",
+       "10       2.397768      0.244951         1.330007        0.008983   \n",
+       "14       2.590127      0.054396         1.155976        0.187602   \n",
+       "17       2.497556      0.085952         1.169808        0.061336   \n",
+       "2        2.467611      0.168399         1.313936        0.109695   \n",
+       "5        2.339322      0.092595         1.209515        0.132825   \n",
+       "20       2.644488      0.119967         1.238554        0.048871   \n",
+       "8        2.456030      0.072136         1.240463        0.092383   \n",
+       "23       2.335796      0.082338         0.786187        0.073499   \n",
+       "11       2.530331      0.107199         1.247842        0.078229   \n",
        "\n",
        "   param_classifier__alpha param_classifier__fit_prior  \\\n",
        "12                       1                        True   \n",
@@ -819,7 +819,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -844,9 +844,9 @@
     },
     {
      "data": {
-      "image/png": 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6uy9yFORV0l/R7/IP8Gx5CtQsxBpUV/XigidxxoO4VY3+JOmme47onPOylZps0RczojzaB6eXtSQ2VPY5n0ysqX5/qcm++uydplqzor4S4/4Sa8bGqffFKZr8nx16ery3NvxOchs4XWp0gtLlcumaa67Rjh07NGnSJL388svy83N/Oc7Ly9O4ceP08ccf67777tOcOXMkSQsXLtRjjz2mwMBAffrppxo6dGipPtetW6eoqBMHpO+++07Tpk1TQECAvvvuu1JJvFdeeUUPP/ywbrzxRu3Zs0fh4eFlxvjyyy/ru+++02WXXVby2tSpU/Xkk0/q0UcfLZOgvOGGGxQXF6eRI0dq7ty5slrdmYm4uDhddNFF+vnnnzVlyhS9+OKLZX7XokWLdPXVV+ujjz4q2e/777/X4MGDNXXqVNWvX19TpkzRAw88ULLPQw89pP/85z965plntGzZMo/+3h9++GF98803atOmjRYtWqTo6OiSbQ6HQ99++23Jn1etWqXly5erS5cu+vXXXxUQcGKhfJfLpdWrVysoKEgATj9XcR7AIfl08lLdZ08kE33P81b9N81KGpmjwo0OFfxpl+95RYeA4v3skt9l3gq+90TG1HqBt+qF+SllbK7yvrerzlinvBudFcX3OM02rK5fJiGwe0uwnr8vRrc9uE/DR8fptgf3a8JwkgYATh6xBqfCwOEpuuHuoyosMGna3S2VmcYt3ihtw+/1yiQfd28J1vP3Buu2h/Zp+Jh43fbQPk34vbtBI8Q/4ZJJThcP5qvpavS3zB9++EF//PGHzj//fP33v/8tSU5Kkp+fn9555x2Fh4frk08+UVpamiTpmWeekeROLv49OSlJ3bp1U6NGJx5g8uqrr0qS7rnnnjIVhg899JDOP/98ZWRk6L333it3jHfddVep5KTkTvAFBwdr3759Onz4cMnrv/32m9atW6c6deronXfeKUkySlLjxo313//+V5I0ffr0cm+LrlOnjmbOnFlqv0GDBqlTp07Kzs5WgwYNSiUnJXd1qOROJNpsVVftJSUlaebMmTKbzfrqq69KJSclycvLS8OGDSv587Fj7oes9O3bt1RyUpJMJpN69+4tf/+Kb3muTfKLqhKt1oqrEosrFvPyTt9JUV5RdYJn46jR1yhqvZLqyErujiupsvTgn5HZ/8RB1/+KsnPMK9ws397uz7zgzxNXjU1V7OfTzkuWaLPkkgo3elalgNMjP7cozvhV/DkUVw/k5VRcqVSsuIrAs/48jxefz2oqh92kZq1zFBb571jmoybLL/qcff0rrmb0K6p0zM2uet7kF1Ut+fpV0l/R78rNqdGnmqgAsQbVVb244Emc8SBuedhf38Gpuv+lA3I63MnJLWsonqgp8ktiQ2Wf88nEmlPTX7HP320qh11q1jpXYVHEGuB0qdFnjd99950kacSIETKbyw41ICBAXbt2ld1u17p165SYmKjNmzfLYrFo9OjRVfZvt9v1+++/S5LGjBlTbptbbrlFkvTLL7+Uu33IkCFlXvPx8VGLFi0kSUePHi15feXKlZKkoUOHql69sqXhl112maKiopSVlaU///yzzPauXbsqNDS0zOutWrUq2f/v6tWrp/r166uwsLDctTf/bvny5SosLFTPnj3Vvn37Ktt36dJFXl5emj17tmbMmFGSsPTUrFmz1LVrV3Xt2lWFjrN7If1jie4MUnhExe8jNDyvVNvTMw53ojgsvOJ1WsJKxhFQYRucft5Fa086Eio+iXIcc5VqWxmvBqZy/79Umyj3687jJ9aT9G5gLvf/S/ddNNbj3K5ppGNFazSFV3JyHFr0Jf1YJes5/b2/yk62w6rRX7HsTIvSU93J7voRrEFotGPxReu+Naz4swiLKizVttL+4txtwhtWvF5lWIOi/uIqXj8ONRexBtV1LL54nUBP4kzVcaG4TXiDyvorqLK/3pem6pE39kuSXrm/hVb/yO25NUlJrGlQWawpKNW20v6OeBJrCkq19YQ71riPffXDiTXA6VKjE5QHDhyQ5K5kNJlM5f4UJzGTk5MVGxsrSSUPkKnK8ePHVVBQILPZrKZNm5bbpjjReOTIkXK3N2nSpNzXi29r/mslZHEfzZs3r3BMlf2+v1Z+/lVgYKBH2z15WE3x3+HfKycr0rJlS73++usqLCzUxIkTFRkZqZYtW+rmm2/W/Pnz5XBUXm01btw4rV+/XuvXr5eP19ldabl/b4gkqWmzLPn4lP++20S7K30PFLU9HeIP11F+vpeCggsV2aD8h0i1aecex/69wadtHKiapa07BNsOOuXKL/8BNIU73XPJu03V4dq7qVmmotDnzCi/v+LXTX8JkZa/9F3hfunF+3FrhJH273TH86atcuTjW0Gc6eB+Au6BoraViT/gr/w8s4JC7BUuFl/c3/5dVfdXzGx2KSCwaA27XM8rFHB67NvmPr42aZMvH9/yLzK06eR+aNr+7VUfi+P2W5WfZ1JQXYeimpb/Ra1t5+L+WLf2bESsQXXtK4odTVrnVRxnOlY3zpjdcaZJ+d9h2lYRt3penKbJb+6XyezSaw+30MolLANQ01Qv1tSpsr/4g3+JNY0riDUxmUW/u+r+ihFrgDOjRicoi5Nb/fr10+jRoyv9adq0qUymk//ifLL7llfZadTvOpmx/N3JjO2uu+5SbGysZs6cqRtvvFEOh0Mff/yxRo4cqa5duyozM/Mfj+tskJLsr727Q2TxcapP//gy2zt0SlZYeJ5Sj/tq5/bTd/XWbjfrz7URkqQBF8WV2R4ZlaPoc47LVmjWujW1/+FFNZlXhNmdpLRJecvL3pJfsMEuZ5JL5vom+cRUfTJk8jaV3MJduK7sSZ7L7lLhJvfrlnYn+vMKN8vS3h0/CsrZz5npkm23+3WfdpyUGSnlmFV7dwTK4uNSn0uSy2zv0DVNYZEFSk320c7NVV+AsNvN+nOV+wvbgCGJZbZHNspTdKcM2QpNWver51/suvdLkdXfqdxsL8UdPLsvPtUGKQk+2rvVXz6+LvUdklZme0yPLIU1sCk1yVs7/6y6st5uM2v9Cvf8GnBl2bszIpsUKLpLjgoLTPq/5VwIOxsRa1BdKQm+J+LM5alltsf0yFRYg0KlJlm0c0PVSWi7zaz1vxTFmSvKiTON8xXdJbsozoSU2d5jYJoee3ufvLxdemNycy1bWPYuNBgvJdGqvduLYs2l5cWadIVFFcWaTVXfmm+3mfXnKvf3rAFDyt7Z5441mUWxxvPvY937HSfWAGdAjU5QNm7cWJI0cuRIzZkzp9KfPn36lFQzxsXFKS+v4ttbi9WvX1++vr5yOp06dOhQuW2KqzgbNmz4j99PcR/FfZ7u33cyiv8Od+/eXa39IiMjdeedd+rjjz/WoUOHtGnTJsXExGjTpk2aNm3a6RhqjfTlp20lSWPv2KaohieqF4ND8jXx3k2SpHmftpXrLwv0Drlqv96d+6MeeHT9KRxHGzmd0sjr96hN9ImTRKufXfc+8qe8vKQli1ooJ7vqW/lwegWOdn8GmdMLZI87UXHgSHUq4xV3ZVLgKB+ZzCfmTM68QiVdm6O0Z8rGuTqjfSSzlLPIpvw1J5KeLodLmW8XyBHvkjnMJL9+pdf4qjPGfXtU1ocFJVWbkuQqcCn95Xy5siVLtFmWmBp92PhX+PJ/7or/sfftV1TjE0tKBNcr1MTH90iS5s1uUjrOXB+vd79Zqwee31G2v9lN3PHilsNq0+HEBSWrn133PrvLHS++aKicrBPrk/paHRp8zRFZ/com1rv1TdHdT7uPIUs+byiHnTlTE3wx3X1B6tZH4xXV9EQ1UnB9myY9714v+4sZkaXmzdDRSXpv+TY9+PrBMv19OSNSTqd0zfhjJdWXkvuJrPe/csg9bz4KU04max2frYg1qK4vZjaQJN36SFzZOPNsbFGbqNJxZtQxvffzFj346v4y/X35TpQ7ztyZoDadTpxXW/0duv/lg+4583G4crJKx5lu/dP1+HR3cvLNx5rpp/lhp/R94tT68n/u759j7z+gqCZ/izVPFsWa//0t1twQr3cXr9UDL+wstz+nUxo59nBJtaQkWf3tuve5oljzeTmx5tojsvqXE2suOK67nymKNZ8Ra85mTplq9U9tUKPPGgcNGqT//e9/mjdvniZOnFhl+8jISHXs2FFbtmzR3Llzdccdd1Ta3tvbW71799by5cs1d+5cPffcc2XaFD8dvH///ifzFkrp16+fJGnx4sVKS0tT3bp1S21funSpEhISFBgYqPPOO+8f/76TceGFF8pisWj16tXauXOn2rVrd1L9dOrUSffcc49uu+02bd68+RSPsub6fWVDLfm6uYZceVAz3v9Zm/4Ml91uVucuSQoItGv1b1FavLBlqX2CgwvUuEm20lLLroNSt16enpy6puTPUQ3cXwKHXbVfffqdWAbguSfOV1rqidvo9u6upzmzOmjsndv06vSV2rwhTDnZFnXolKK69Qq0a0ddzf3fOaf67eMk+F1oUcFwh3K/sinpphz5dvWSydukgvV2uXIkaz9vBVxd+sE1jnSX7LFOmeuVPRBZWnsp6F5fZb5eoNT78mQ5xyyvcLNsexxyHHHJFCjVe8FPJmvpfa19vRVwg0U5n9qUcnuufDp4yRxsUuEOh5zJ7qRm3Wf9/lGlOk6N338K15LP0zTkuqOa8dU6bVpTV3a7SZ17pCmgjkOrl4Vq8Well/wIDrGpcfNcpaWUvSixd3uQ5rzRQmPvP6BXP9qgzf8Xopwsb3Xomq669W3atTlIc99sUWofb4tTk57co9sf2qf9OwOVnGiVt8Wpxi1y1aRFbtE4w/TR9IqXNMGZteq7ulo8N0xDRyXrnZ92aOOqIDlsJnXunamAIKd+/yFEi+eEl9onuJ5djVsVKC257MOz9mwJ0PvTGuq2x47o9YW7tGl1HeVkeiumR5bqhtm1c0OAPny5/Iutr3+9q+T/Q4vWpOszOE2tO574Yjr9iSYlt6b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",
       "text/plain": [
-       "<Figure size 2160x1080 with 2 Axes>"
+       "<Figure size 1440x1296 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -860,9 +860,9 @@
     "y_predicted = classifier.predict(X_test)\n",
     "\n",
     "%matplotlib inline\n",
-    "plt.rcParams[\"figure.figsize\"] = (30, 15)\n",
+    "plt.rcParams[\"figure.figsize\"] = (20, 18)\n",
     "plt.rcParams[\"figure.facecolor\"] = \"white\"\n",
-    "plt.rcParams[\"font.size\"] = 12\n",
+    "plt.rcParams[\"font.size\"] = 22\n",
     "plt.rcParams[\"axes.xmargin\"] = 0\n",
     "\n",
     "print(metrics.classification_report(y_test, y_predicted, digits=4))\n",
@@ -873,7 +873,8 @@
     "    normalize=\"pred\",\n",
     "    values_format=\".2f\",\n",
     ")\n",
-    "None"
+    "plt.tight_layout()\n",
+    "plt.savefig(\"mag-confusion.png\", dpi=600)"
    ]
   }
  ],
diff --git a/examples/simple/deploy/deploy.ipynb b/examples/simple/deploy/deploy.ipynb
index d5072ab..abe5ac8 100644
--- a/examples/simple/deploy/deploy.ipynb
+++ b/examples/simple/deploy/deploy.ipynb
@@ -31,7 +31,7 @@
       "\u001b[38;5;226mCannot find credentials files, defaulting to using ParallelTinyDbDriver\u001b[0m\n",
       "\u001b[38;5;226mThe selected tracing database (ParallelTinyDbDriver) is not recommended for production\u001b[0m\n",
       "\u001b[38;5;226mCannot find credentials files, defaulting to using LargeFileLocal\u001b[0m\n",
-      "\u001b[38;5;39mGreatAI (v0.1.4): configured ✅\u001b[0m\n",
+      "\u001b[38;5;39mGreatAI (v0.1.6): configured ✅\u001b[0m\n",
       "\u001b[38;5;39m  🔩 tracing_database: ParallelTinyDbDriver\u001b[0m\n",
       "\u001b[38;5;39m  🔩 large_file_implementation: LargeFileLocal\u001b[0m\n",
       "\u001b[38;5;39m  🔩 is_production: False\u001b[0m\n",
@@ -41,8 +41,8 @@
       "\u001b[38;5;226mYou still need to check whether you follow all best practices before trusting your deployment.\u001b[0m\n",
       "\u001b[38;5;226m> Find out more at https://se-ml.github.io/practices\u001b[0m\n",
       "\u001b[38;5;39mFetching cached versions of small-domain-prediction\u001b[0m\n",
-      "\u001b[38;5;39mLatest version of small-domain-prediction is 0 (from versions: 0)\u001b[0m\n",
-      "\u001b[38;5;39mFile small-domain-prediction-0 found in cache\u001b[0m\n"
+      "\u001b[38;5;39mLatest version of small-domain-prediction is 2 (from versions: 0, 1, 2)\u001b[0m\n",
+      "\u001b[38;5;39mFile small-domain-prediction-2 found in cache\u001b[0m\n"
      ]
     }
    ],
@@ -136,7 +136,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 12332/12332 [01:27<00:00, 141.08it/s]\n"
+      "100%|██████████| 12272/12272 [01:10<00:00, 174.77it/s]\n"
      ]
     },
     {
@@ -145,37 +145,37 @@
      "text": [
       "                       precision    recall  f1-score   support\n",
       "\n",
-      "                  Art       0.43      0.35      0.39       125\n",
-      "              Biology       0.77      0.83      0.80      1209\n",
-      "             Business       0.50      0.71      0.59       312\n",
-      "            Chemistry       0.80      0.67      0.73      1194\n",
-      "     Computer Science       0.77      0.76      0.77      1293\n",
-      "            Economics       0.64      0.58      0.61       251\n",
-      "          Engineering       0.55      0.53      0.54       815\n",
-      "Environmental Science       0.54      0.56      0.55       230\n",
-      "            Geography       0.54      0.44      0.48       277\n",
-      "              Geology       0.76      0.67      0.71       228\n",
-      "              History       0.29      0.21      0.24       102\n",
-      "    Materials Science       0.73      0.81      0.77      1053\n",
-      "          Mathematics       0.80      0.70      0.75       551\n",
-      "             Medicine       0.95      0.77      0.85      2794\n",
-      "           Philosophy       0.61      0.12      0.21        88\n",
-      "              Physics       0.67      0.76      0.72       605\n",
-      "    Political Science       0.43      0.52      0.47       297\n",
-      "           Psychology       0.53      0.80      0.63       600\n",
-      "            Sociology       0.32      0.60      0.42       308\n",
+      "                  Art       0.54      0.38      0.45       126\n",
+      "              Biology       0.77      0.84      0.80      1215\n",
+      "             Business       0.47      0.73      0.57       311\n",
+      "            Chemistry       0.82      0.67      0.74      1205\n",
+      "     Computer Science       0.77      0.76      0.76      1277\n",
+      "            Economics       0.69      0.55      0.61       270\n",
+      "          Engineering       0.55      0.52      0.53       754\n",
+      "Environmental Science       0.56      0.55      0.55       227\n",
+      "            Geography       0.54      0.39      0.45       276\n",
+      "              Geology       0.74      0.67      0.70       265\n",
+      "              History       0.35      0.18      0.24       140\n",
+      "    Materials Science       0.72      0.81      0.76      1011\n",
+      "          Mathematics       0.77      0.70      0.74       498\n",
+      "             Medicine       0.96      0.77      0.86      2835\n",
+      "           Philosophy       0.57      0.06      0.10        71\n",
+      "              Physics       0.66      0.75      0.70       611\n",
+      "    Political Science       0.44      0.61      0.51       291\n",
+      "           Psychology       0.52      0.84      0.64       574\n",
+      "            Sociology       0.33      0.59      0.42       315\n",
       "\n",
-      "             accuracy                           0.71     12332\n",
-      "            macro avg       0.61      0.60      0.59     12332\n",
-      "         weighted avg       0.73      0.71      0.71     12332\n",
+      "             accuracy                           0.71     12272\n",
+      "            macro avg       0.62      0.60      0.59     12272\n",
+      "         weighted avg       0.74      0.71      0.71     12272\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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       "text/plain": [
-       "<Figure size 2160x1080 with 2 Axes>"
+       "<Figure size 1368x1296 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -202,9 +202,9 @@
     "\n",
     "    # Configure matplotlib to have nice, high-resolution charts\n",
     "    %matplotlib inline\n",
-    "    plt.rcParams[\"figure.figsize\"] = (30, 15)\n",
+    "    plt.rcParams[\"figure.figsize\"] = (19, 18)\n",
     "    plt.rcParams[\"figure.facecolor\"] = \"white\"\n",
-    "    plt.rcParams[\"font.size\"] = 12\n",
+    "    plt.rcParams[\"font.size\"] = 16\n",
     "    plt.rcParams[\"axes.xmargin\"] = 0\n",
     "\n",
     "    print(metrics.classification_report(y_actual_aligned, y_predicted))\n",
@@ -214,7 +214,10 @@
     "        xticks_rotation=\"vertical\",\n",
     "        normalize=\"pred\",\n",
     "        values_format=\".2f\",\n",
-    "    )"
+    "    )\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.savefig(\"ss-confusion.png\", dpi=600)"
    ]
   }
  ],
diff --git a/examples/simple/deploy/index.html b/examples/simple/deploy/index.html
index 837ea92..ad6830e 100644
--- a/examples/simple/deploy/index.html
+++ b/examples/simple/deploy/index.html
@@ -2041,7 +2041,7 @@ def get_label(
 <span style="color: rgb(255,255,0)">Cannot find credentials files, defaulting to using ParallelTinyDbDriver</span>
 <span style="color: rgb(255,255,0)">The selected tracing database (ParallelTinyDbDriver) is not recommended for production</span>
 <span style="color: rgb(255,255,0)">Cannot find credentials files, defaulting to using LargeFileLocal</span>
-<span style="color: rgb(0,175,255)">GreatAI (v0.1.4): configured ✅</span>
+<span style="color: rgb(0,175,255)">GreatAI (v0.1.6): configured ✅</span>
 <span style="color: rgb(0,175,255)">  🔩 tracing_database: ParallelTinyDbDriver</span>
 <span style="color: rgb(0,175,255)">  🔩 large_file_implementation: LargeFileLocal</span>
 <span style="color: rgb(0,175,255)">  🔩 is_production: False</span>
@@ -2051,8 +2051,8 @@ def get_label(
 <span style="color: rgb(255,255,0)">You still need to check whether you follow all best practices before trusting your deployment.</span>
 <span style="color: rgb(255,255,0)">&gt; Find out more at https://se-ml.github.io/practices</span>
 <span style="color: rgb(0,175,255)">Fetching cached versions of small-domain-prediction</span>
-<span style="color: rgb(0,175,255)">Latest version of small-domain-prediction is 0 (from versions: 0)</span>
-<span style="color: rgb(0,175,255)">File small-domain-prediction-0 found in cache</span>
+<span style="color: rgb(0,175,255)">Latest version of small-domain-prediction is 2 (from versions: 0, 1, 2)</span>
+<span style="color: rgb(0,175,255)">File small-domain-prediction-2 found in cache</span>
 </pre>
 </div>
 </div>
@@ -2114,9 +2114,9 @@ def get_label(
 
     <span class="c1"># Configure matplotlib to have nice, high-resolution charts</span>
     <span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">&quot;figure.figsize&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">15</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">&quot;figure.figsize&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">19</span><span class="p">,</span> <span class="mi">18</span><span class="p">)</span>
     <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">&quot;figure.facecolor&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;white&quot;</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">&quot;font.size&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">12</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">&quot;font.size&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">16</span>
     <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">&quot;axes.xmargin&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
 
     <span class="nb">print</span><span class="p">(</span><span class="n">metrics</span><span class="o">.</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_actual_aligned</span><span class="p">,</span> <span class="n">y_predicted</span><span class="p">))</span>
@@ -2127,6 +2127,9 @@ def get_label(
         <span class="n">normalize</span><span class="o">=</span><span class="s2">&quot;pred&quot;</span><span class="p">,</span>
         <span class="n">values_format</span><span class="o">=</span><span class="s2">&quot;.2f&quot;</span><span class="p">,</span>
     <span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s2">&quot;ss-confusion.png&quot;</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">600</span><span class="p">)</span>
 </pre></div>
 <div id="cell-2" class="clipboard-copy-txt">if __name__ == "__main__":
     from great_ai import query_ground_truth
@@ -2147,9 +2150,9 @@ def get_label(
 
     # Configure matplotlib to have nice, high-resolution charts
     %matplotlib inline
-    plt.rcParams["figure.figsize"] = (30, 15)
+    plt.rcParams["figure.figsize"] = (19, 18)
     plt.rcParams["figure.facecolor"] = "white"
-    plt.rcParams["font.size"] = 12
+    plt.rcParams["font.size"] = 16
     plt.rcParams["axes.xmargin"] = 0
 
     print(metrics.classification_report(y_actual_aligned, y_predicted))
@@ -2159,7 +2162,10 @@ def get_label(
         xticks_rotation="vertical",
         normalize="pred",
         values_format=".2f",
-    )</div>
+    )
+
+    plt.tight_layout()
+    plt.savefig("ss-confusion.png", dpi=600)</div>
     
         </div>
     </div>
@@ -2181,7 +2187,7 @@ def get_label(
 
 
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
-<pre>100%|██████████| 12332/12332 [01:27&lt;00:00, 141.08it/s]
+<pre>100%|██████████| 12272/12272 [01:10&lt;00:00, 174.77it/s]
 </pre>
 </div>
 </div>
@@ -2195,29 +2201,29 @@ def get_label(
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>                       precision    recall  f1-score   support
 
-                  Art       0.43      0.35      0.39       125
-              Biology       0.77      0.83      0.80      1209
-             Business       0.50      0.71      0.59       312
-            Chemistry       0.80      0.67      0.73      1194
-     Computer Science       0.77      0.76      0.77      1293
-            Economics       0.64      0.58      0.61       251
-          Engineering       0.55      0.53      0.54       815
-Environmental Science       0.54      0.56      0.55       230
-            Geography       0.54      0.44      0.48       277
-              Geology       0.76      0.67      0.71       228
-              History       0.29      0.21      0.24       102
-    Materials Science       0.73      0.81      0.77      1053
-          Mathematics       0.80      0.70      0.75       551
-             Medicine       0.95      0.77      0.85      2794
-           Philosophy       0.61      0.12      0.21        88
-              Physics       0.67      0.76      0.72       605
-    Political Science       0.43      0.52      0.47       297
-           Psychology       0.53      0.80      0.63       600
-            Sociology       0.32      0.60      0.42       308
+                  Art       0.54      0.38      0.45       126
+              Biology       0.77      0.84      0.80      1215
+             Business       0.47      0.73      0.57       311
+            Chemistry       0.82      0.67      0.74      1205
+     Computer Science       0.77      0.76      0.76      1277
+            Economics       0.69      0.55      0.61       270
+          Engineering       0.55      0.52      0.53       754
+Environmental Science       0.56      0.55      0.55       227
+            Geography       0.54      0.39      0.45       276
+              Geology       0.74      0.67      0.70       265
+              History       0.35      0.18      0.24       140
+    Materials Science       0.72      0.81      0.76      1011
+          Mathematics       0.77      0.70      0.74       498
+             Medicine       0.96      0.77      0.86      2835
+           Philosophy       0.57      0.06      0.10        71
+              Physics       0.66      0.75      0.70       611
+    Political Science       0.44      0.61      0.51       291
+           Psychology       0.52      0.84      0.64       574
+            Sociology       0.33      0.59      0.42       315
 
-             accuracy                           0.71     12332
-            macro avg       0.61      0.60      0.59     12332
-         weighted avg       0.73      0.71      0.71     12332
+             accuracy                           0.71     12272
+            macro avg       0.62      0.60      0.59     12272
+         weighted avg       0.74      0.71      0.71     12272
 
 </pre>
 </div>
@@ -2232,7 +2238,7 @@ Environmental Science       0.54      0.56      0.55       230
 
 
 <div class="jp-RenderedImage jp-OutputArea-output ">
-<img src="data:image/png;base64,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"
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 class="
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@@ -2255,7 +2261,7 @@ class="
   <small>
     
       Last update:
-      <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">July 13, 2022</span>
+      <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">July 16, 2022</span>
       
     
   </small>
diff --git a/examples/simple/ss-confusion.png b/examples/simple/ss-confusion.png
new file mode 100644
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diff --git a/examples/simple/train/index.html b/examples/simple/train/index.html
index 473d970..e9254eb 100644
--- a/examples/simple/train/index.html
+++ b/examples/simple/train/index.html
@@ -2130,6 +2130,7 @@ def create_pipeline() -> Pipeline:
             </clipboard-copy>
         </div>
             <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">GridSearchCV</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 
 <span class="n">optimisation_pipeline</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span>
     <span class="n">create_pipeline</span><span class="p">(),</span>
@@ -2150,6 +2151,7 @@ def create_pipeline() -> Pipeline:
 <span class="n">results</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s2">&quot;rank_test_score&quot;</span><span class="p">)</span>
 </pre></div>
 <div id="cell-4" class="clipboard-copy-txt">from sklearn.model_selection import GridSearchCV
+import pandas as pd
 
 optimisation_pipeline = GridSearchCV(
     create_pipeline(),
@@ -2197,25 +2199,481 @@ results.sort_values("rank_test_score")</div>
 <div class="jp-OutputArea-child">
 
     
-    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[4]:</div>
 
 
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
-<pre>
-<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
-<span class="ansi-red-fg">NameError</span>                                 Traceback (most recent call last)
-<span class="ansi-green-intense-fg ansi-bold">/data/projects/great-ai/docs/examples/simple/train.ipynb Cell 6</span> in <span class="ansi-cyan-fg">&lt;cell line: 18&gt;</span><span class="ansi-blue-fg">()</span>
-<span class="ansi-green-intense-fg ansi-bold">      &lt;a href=&#39;vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=2&#39;&gt;3&lt;/a&gt;</span> optimisation_pipeline = GridSearchCV(
-<span class="ansi-green-intense-fg ansi-bold">      &lt;a href=&#39;vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=3&#39;&gt;4&lt;/a&gt;</span>     create_pipeline(),
-<span class="ansi-green-intense-fg ansi-bold">      &lt;a href=&#39;vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=4&#39;&gt;5&lt;/a&gt;</span>     {
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">     &lt;a href=&#39;vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=13&#39;&gt;14&lt;/a&gt;</span>     verbose=1,
-<span class="ansi-green-intense-fg ansi-bold">     &lt;a href=&#39;vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=14&#39;&gt;15&lt;/a&gt;</span> )
-<span class="ansi-green-intense-fg ansi-bold">     &lt;a href=&#39;vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=15&#39;&gt;16&lt;/a&gt;</span> optimisation_pipeline.fit(X, y)
-<span class="ansi-green-fg">---&gt; &lt;a href=&#39;vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=17&#39;&gt;18&lt;/a&gt;</span> results = pd.DataFrame(optimisation_pipeline.cv_results_)
-<span class="ansi-green-intense-fg ansi-bold">     &lt;a href=&#39;vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=18&#39;&gt;19&lt;/a&gt;</span> results.sort_values(&#34;rank_test_score&#34;)
 
-<span class="ansi-red-fg">NameError</span>: name &#39;pd&#39; is not defined</pre>
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>mean_fit_time</th>
+      <th>std_fit_time</th>
+      <th>mean_score_time</th>
+      <th>std_score_time</th>
+      <th>param_classifier__alpha</th>
+      <th>param_classifier__fit_prior</th>
+      <th>param_vectorizer__max_df</th>
+      <th>param_vectorizer__min_df</th>
+      <th>params</th>
+      <th>split0_test_score</th>
+      <th>split1_test_score</th>
+      <th>split2_test_score</th>
+      <th>mean_test_score</th>
+      <th>std_test_score</th>
+      <th>rank_test_score</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>7</th>
+      <td>14.549476</td>
+      <td>0.361685</td>
+      <td>8.476837</td>
+      <td>0.222398</td>
+      <td>0.5</td>
+      <td>False</td>
+      <td>0.05</td>
+      <td>20</td>
+      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
+      <td>0.518061</td>
+      <td>0.514842</td>
+      <td>0.511599</td>
+      <td>0.514834</td>
+      <td>0.002638</td>
+      <td>1</td>
+    </tr>
+    <tr>
+      <th>10</th>
+      <td>11.235289</td>
+      <td>0.130426</td>
+      <td>4.092868</td>
+      <td>0.082518</td>
+      <td>0.5</td>
+      <td>False</td>
+      <td>0.1</td>
+      <td>20</td>
+      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
+      <td>0.513897</td>
+      <td>0.515661</td>
+      <td>0.507867</td>
+      <td>0.512475</td>
+      <td>0.003337</td>
+      <td>2</td>
+    </tr>
+    <tr>
+      <th>19</th>
+      <td>7.383645</td>
+      <td>0.138110</td>
+      <td>4.130709</td>
+      <td>0.250048</td>
+      <td>1</td>
+      <td>False</td>
+      <td>0.05</td>
+      <td>20</td>
+      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
+      <td>0.496825</td>
+      <td>0.501045</td>
+      <td>0.496854</td>
+      <td>0.498241</td>
+      <td>0.001983</td>
+      <td>3</td>
+    </tr>
+    <tr>
+      <th>11</th>
+      <td>10.435154</td>
+      <td>0.305144</td>
+      <td>3.882101</td>
+      <td>0.128886</td>
+      <td>0.5</td>
+      <td>False</td>
+      <td>0.1</td>
+      <td>100</td>
+      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
+      <td>0.493247</td>
+      <td>0.497814</td>
+      <td>0.502245</td>
+      <td>0.497769</td>
+      <td>0.003674</td>
+      <td>4</td>
+    </tr>
+    <tr>
+      <th>8</th>
+      <td>13.643193</td>
+      <td>0.310696</td>
+      <td>4.173707</td>
+      <td>0.142980</td>
+      <td>0.5</td>
+      <td>False</td>
+      <td>0.05</td>
+      <td>100</td>
+      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
+      <td>0.489609</td>
+      <td>0.495207</td>
+      <td>0.498154</td>
+      <td>0.494323</td>
+      <td>0.003544</td>
+      <td>5</td>
+    </tr>
+    <tr>
+      <th>22</th>
+      <td>7.048340</td>
+      <td>0.050070</td>
+      <td>3.172948</td>
+      <td>0.152418</td>
+      <td>1</td>
+      <td>False</td>
+      <td>0.1</td>
+      <td>20</td>
+      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
+      <td>0.487456</td>
+      <td>0.493865</td>
+      <td>0.491157</td>
+      <td>0.490826</td>
+      <td>0.002627</td>
+      <td>6</td>
+    </tr>
+    <tr>
+      <th>23</th>
+      <td>7.564685</td>
+      <td>0.146092</td>
+      <td>2.374111</td>
+      <td>0.285026</td>
+      <td>1</td>
+      <td>False</td>
+      <td>0.1</td>
+      <td>100</td>
+      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
+      <td>0.485160</td>
+      <td>0.494039</td>
+      <td>0.490127</td>
+      <td>0.489776</td>
+      <td>0.003633</td>
+      <td>7</td>
+    </tr>
+    <tr>
+      <th>20</th>
+      <td>7.172353</td>
+      <td>0.212599</td>
+      <td>3.747219</td>
+      <td>0.130217</td>
+      <td>1</td>
+      <td>False</td>
+      <td>0.05</td>
+      <td>100</td>
+      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
+      <td>0.481303</td>
+      <td>0.490002</td>
+      <td>0.488269</td>
+      <td>0.486524</td>
+      <td>0.003759</td>
+      <td>8</td>
+    </tr>
+    <tr>
+      <th>6</th>
+      <td>14.276345</td>
+      <td>0.456576</td>
+      <td>8.318859</td>
+      <td>0.268701</td>
+      <td>0.5</td>
+      <td>False</td>
+      <td>0.05</td>
+      <td>5</td>
+      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
+      <td>0.482429</td>
+      <td>0.487735</td>
+      <td>0.484888</td>
+      <td>0.485017</td>
+      <td>0.002168</td>
+      <td>9</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>14.902358</td>
+      <td>0.737693</td>
+      <td>5.975091</td>
+      <td>0.171150</td>
+      <td>0.5</td>
+      <td>True</td>
+      <td>0.05</td>
+      <td>100</td>
+      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
+      <td>0.469598</td>
+      <td>0.474490</td>
+      <td>0.473637</td>
+      <td>0.472575</td>
+      <td>0.002134</td>
+      <td>10</td>
+    </tr>
+    <tr>
+      <th>9</th>
+      <td>12.677349</td>
+      <td>0.145143</td>
+      <td>4.374204</td>
+      <td>0.175674</td>
+      <td>0.5</td>
+      <td>False</td>
+      <td>0.1</td>
+      <td>5</td>
+      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
+      <td>0.468872</td>
+      <td>0.476451</td>
+      <td>0.470921</td>
+      <td>0.472082</td>
+      <td>0.003201</td>
+      <td>11</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>13.423686</td>
+      <td>0.482872</td>
+      <td>8.008324</td>
+      <td>0.442975</td>
+      <td>0.5</td>
+      <td>True</td>
+      <td>0.1</td>
+      <td>100</td>
+      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
+      <td>0.465726</td>
+      <td>0.474548</td>
+      <td>0.471879</td>
+      <td>0.470718</td>
+      <td>0.003694</td>
+      <td>12</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>13.819117</td>
+      <td>0.838347</td>
+      <td>6.161175</td>
+      <td>0.336590</td>
+      <td>0.5</td>
+      <td>True</td>
+      <td>0.05</td>
+      <td>20</td>
+      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
+      <td>0.463395</td>
+      <td>0.473982</td>
+      <td>0.471262</td>
+      <td>0.469546</td>
+      <td>0.004489</td>
+      <td>13</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>13.281476</td>
+      <td>0.588822</td>
+      <td>8.335852</td>
+      <td>0.254627</td>
+      <td>0.5</td>
+      <td>True</td>
+      <td>0.1</td>
+      <td>20</td>
+      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
+      <td>0.458734</td>
+      <td>0.468053</td>
+      <td>0.464418</td>
+      <td>0.463735</td>
+      <td>0.003835</td>
+      <td>14</td>
+    </tr>
+    <tr>
+      <th>14</th>
+      <td>7.282247</td>
+      <td>0.444940</td>
+      <td>3.567094</td>
+      <td>0.044519</td>
+      <td>1</td>
+      <td>True</td>
+      <td>0.05</td>
+      <td>100</td>
+      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
+      <td>0.438189</td>
+      <td>0.450160</td>
+      <td>0.446180</td>
+      <td>0.444843</td>
+      <td>0.004978</td>
+      <td>15</td>
+    </tr>
+    <tr>
+      <th>17</th>
+      <td>7.098797</td>
+      <td>0.196241</td>
+      <td>3.838628</td>
+      <td>0.091128</td>
+      <td>1</td>
+      <td>True</td>
+      <td>0.1</td>
+      <td>100</td>
+      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
+      <td>0.436488</td>
+      <td>0.444503</td>
+      <td>0.445900</td>
+      <td>0.442297</td>
+      <td>0.004147</td>
+      <td>16</td>
+    </tr>
+    <tr>
+      <th>18</th>
+      <td>7.492791</td>
+      <td>0.288889</td>
+      <td>3.843224</td>
+      <td>0.073438</td>
+      <td>1</td>
+      <td>False</td>
+      <td>0.05</td>
+      <td>5</td>
+      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
+      <td>0.428196</td>
+      <td>0.431945</td>
+      <td>0.430160</td>
+      <td>0.430100</td>
+      <td>0.001531</td>
+      <td>17</td>
+    </tr>
+    <tr>
+      <th>21</th>
+      <td>7.229826</td>
+      <td>0.099823</td>
+      <td>3.656332</td>
+      <td>0.073780</td>
+      <td>1</td>
+      <td>False</td>
+      <td>0.1</td>
+      <td>5</td>
+      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
+      <td>0.403130</td>
+      <td>0.410170</td>
+      <td>0.409801</td>
+      <td>0.407700</td>
+      <td>0.003235</td>
+      <td>18</td>
+    </tr>
+    <tr>
+      <th>13</th>
+      <td>7.158370</td>
+      <td>0.169818</td>
+      <td>3.765632</td>
+      <td>0.082924</td>
+      <td>1</td>
+      <td>True</td>
+      <td>0.05</td>
+      <td>20</td>
+      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
+      <td>0.399237</td>
+      <td>0.412872</td>
+      <td>0.407982</td>
+      <td>0.406697</td>
+      <td>0.005640</td>
+      <td>19</td>
+    </tr>
+    <tr>
+      <th>16</th>
+      <td>7.064643</td>
+      <td>0.119529</td>
+      <td>3.810983</td>
+      <td>0.125897</td>
+      <td>1</td>
+      <td>True</td>
+      <td>0.1</td>
+      <td>20</td>
+      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
+      <td>0.388060</td>
+      <td>0.399247</td>
+      <td>0.396325</td>
+      <td>0.394544</td>
+      <td>0.004737</td>
+      <td>20</td>
+    </tr>
+    <tr>
+      <th>0</th>
+      <td>13.749660</td>
+      <td>0.465174</td>
+      <td>6.407841</td>
+      <td>0.549166</td>
+      <td>0.5</td>
+      <td>True</td>
+      <td>0.05</td>
+      <td>5</td>
+      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
+      <td>0.384852</td>
+      <td>0.386487</td>
+      <td>0.386796</td>
+      <td>0.386045</td>
+      <td>0.000853</td>
+      <td>21</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>15.954147</td>
+      <td>0.318013</td>
+      <td>6.337361</td>
+      <td>0.261697</td>
+      <td>0.5</td>
+      <td>True</td>
+      <td>0.1</td>
+      <td>5</td>
+      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
+      <td>0.369785</td>
+      <td>0.375645</td>
+      <td>0.375858</td>
+      <td>0.373763</td>
+      <td>0.002814</td>
+      <td>22</td>
+    </tr>
+    <tr>
+      <th>12</th>
+      <td>7.120198</td>
+      <td>0.050452</td>
+      <td>3.833905</td>
+      <td>0.069540</td>
+      <td>1</td>
+      <td>True</td>
+      <td>0.05</td>
+      <td>5</td>
+      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
+      <td>0.277741</td>
+      <td>0.280564</td>
+      <td>0.285337</td>
+      <td>0.281214</td>
+      <td>0.003135</td>
+      <td>23</td>
+    </tr>
+    <tr>
+      <th>15</th>
+      <td>7.497707</td>
+      <td>0.183054</td>
+      <td>3.870714</td>
+      <td>0.062888</td>
+      <td>1</td>
+      <td>True</td>
+      <td>0.1</td>
+      <td>5</td>
+      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
+      <td>0.255578</td>
+      <td>0.263381</td>
+      <td>0.266184</td>
+      <td>0.261714</td>
+      <td>0.004487</td>
+      <td>24</td>
+    </tr>
+  </tbody>
+</table>
 </div>
 </div>
 
@@ -2223,6 +2681,8 @@ results.sort_values("rank_test_score")</div>
 
 </div>
 
+</div>
+
 </div>
 </div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
 <div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
@@ -2230,7 +2690,7 @@ results.sort_values("rank_test_score")</div>
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
         
         
         <div class="CodeMirror cm-s-jupyter">
@@ -2277,7 +2737,7 @@ classifier.fit(X, y)</div>
 <div class="jp-OutputArea-child">
 
     
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[&nbsp;]:</div>
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[5]:</div>
 
 
 
@@ -2313,7 +2773,7 @@ classifier.fit(X, y)</div>
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
         
         
         <div class="CodeMirror cm-s-jupyter">
@@ -2359,9 +2819,9 @@ save_model(classifier, key="small-domain-prediction", keep_last_n=5)</div>
 
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
 <pre><span style="color: rgb(0,175,255)">Fetching cached versions of small-domain-prediction</span>
-<span style="color: rgb(0,175,255)">Copying file for small-domain-prediction-1</span>
-<span style="color: rgb(0,175,255)">Compressing small-domain-prediction-1</span>
-<span style="color: rgb(0,175,255)">Model small-domain-prediction uploaded with version 1</span>
+<span style="color: rgb(0,175,255)">Copying file for small-domain-prediction-2</span>
+<span style="color: rgb(0,175,255)">Compressing small-domain-prediction-2</span>
+<span style="color: rgb(0,175,255)">Model small-domain-prediction uploaded with version 2</span>
 </pre>
 </div>
 </div>
@@ -2369,13 +2829,13 @@ save_model(classifier, key="small-domain-prediction", keep_last_n=5)</div>
 <div class="jp-OutputArea-child">
 
     
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[&nbsp;]:</div>
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[6]:</div>
 
 
 
 
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
-<pre>&#39;small-domain-prediction:1&#39;</pre>
+<pre>&#39;small-domain-prediction:2&#39;</pre>
 </div>
 
 </div>
diff --git a/examples/simple/train/train.ipynb b/examples/simple/train/train.ipynb
index d2d94c5..1ba1e87 100644
--- a/examples/simple/train/train.ipynb
+++ b/examples/simple/train/train.ipynb
@@ -122,19 +122,646 @@
      ]
     },
     {
-     "ename": "NameError",
-     "evalue": "name 'pd' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m/data/projects/great-ai/docs/examples/simple/train.ipynb Cell 6\u001b[0m in \u001b[0;36m<cell line: 18>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=2'>3</a>\u001b[0m optimisation_pipeline \u001b[39m=\u001b[39m GridSearchCV(\n\u001b[1;32m      <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=3'>4</a>\u001b[0m     create_pipeline(),\n\u001b[1;32m      <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=4'>5</a>\u001b[0m     {\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=13'>14</a>\u001b[0m     verbose\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m,\n\u001b[1;32m     <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=14'>15</a>\u001b[0m )\n\u001b[1;32m     <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=15'>16</a>\u001b[0m optimisation_pipeline\u001b[39m.\u001b[39mfit(X, y)\n\u001b[0;32m---> <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=17'>18</a>\u001b[0m results \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mDataFrame(optimisation_pipeline\u001b[39m.\u001b[39mcv_results_)\n\u001b[1;32m     <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=18'>19</a>\u001b[0m results\u001b[39m.\u001b[39msort_values(\u001b[39m\"\u001b[39m\u001b[39mrank_test_score\u001b[39m\u001b[39m\"\u001b[39m)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
-     ]
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean_fit_time</th>\n",
+       "      <th>std_fit_time</th>\n",
+       "      <th>mean_score_time</th>\n",
+       "      <th>std_score_time</th>\n",
+       "      <th>param_classifier__alpha</th>\n",
+       "      <th>param_classifier__fit_prior</th>\n",
+       "      <th>param_vectorizer__max_df</th>\n",
+       "      <th>param_vectorizer__min_df</th>\n",
+       "      <th>params</th>\n",
+       "      <th>split0_test_score</th>\n",
+       "      <th>split1_test_score</th>\n",
+       "      <th>split2_test_score</th>\n",
+       "      <th>mean_test_score</th>\n",
+       "      <th>std_test_score</th>\n",
+       "      <th>rank_test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>14.549476</td>\n",
+       "      <td>0.361685</td>\n",
+       "      <td>8.476837</td>\n",
+       "      <td>0.222398</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>20</td>\n",
+       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
+       "      <td>0.518061</td>\n",
+       "      <td>0.514842</td>\n",
+       "      <td>0.511599</td>\n",
+       "      <td>0.514834</td>\n",
+       "      <td>0.002638</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>11.235289</td>\n",
+       "      <td>0.130426</td>\n",
+       "      <td>4.092868</td>\n",
+       "      <td>0.082518</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>20</td>\n",
+       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
+       "      <td>0.513897</td>\n",
+       "      <td>0.515661</td>\n",
+       "      <td>0.507867</td>\n",
+       "      <td>0.512475</td>\n",
+       "      <td>0.003337</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>7.383645</td>\n",
+       "      <td>0.138110</td>\n",
+       "      <td>4.130709</td>\n",
+       "      <td>0.250048</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>20</td>\n",
+       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
+       "      <td>0.496825</td>\n",
+       "      <td>0.501045</td>\n",
+       "      <td>0.496854</td>\n",
+       "      <td>0.498241</td>\n",
+       "      <td>0.001983</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>10.435154</td>\n",
+       "      <td>0.305144</td>\n",
+       "      <td>3.882101</td>\n",
+       "      <td>0.128886</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
+       "      <td>0.493247</td>\n",
+       "      <td>0.497814</td>\n",
+       "      <td>0.502245</td>\n",
+       "      <td>0.497769</td>\n",
+       "      <td>0.003674</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>13.643193</td>\n",
+       "      <td>0.310696</td>\n",
+       "      <td>4.173707</td>\n",
+       "      <td>0.142980</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
+       "      <td>0.489609</td>\n",
+       "      <td>0.495207</td>\n",
+       "      <td>0.498154</td>\n",
+       "      <td>0.494323</td>\n",
+       "      <td>0.003544</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>7.048340</td>\n",
+       "      <td>0.050070</td>\n",
+       "      <td>3.172948</td>\n",
+       "      <td>0.152418</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>20</td>\n",
+       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
+       "      <td>0.487456</td>\n",
+       "      <td>0.493865</td>\n",
+       "      <td>0.491157</td>\n",
+       "      <td>0.490826</td>\n",
+       "      <td>0.002627</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>7.564685</td>\n",
+       "      <td>0.146092</td>\n",
+       "      <td>2.374111</td>\n",
+       "      <td>0.285026</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
+       "      <td>0.485160</td>\n",
+       "      <td>0.494039</td>\n",
+       "      <td>0.490127</td>\n",
+       "      <td>0.489776</td>\n",
+       "      <td>0.003633</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>7.172353</td>\n",
+       "      <td>0.212599</td>\n",
+       "      <td>3.747219</td>\n",
+       "      <td>0.130217</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
+       "      <td>0.481303</td>\n",
+       "      <td>0.490002</td>\n",
+       "      <td>0.488269</td>\n",
+       "      <td>0.486524</td>\n",
+       "      <td>0.003759</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>14.276345</td>\n",
+       "      <td>0.456576</td>\n",
+       "      <td>8.318859</td>\n",
+       "      <td>0.268701</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>5</td>\n",
+       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
+       "      <td>0.482429</td>\n",
+       "      <td>0.487735</td>\n",
+       "      <td>0.484888</td>\n",
+       "      <td>0.485017</td>\n",
+       "      <td>0.002168</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>14.902358</td>\n",
+       "      <td>0.737693</td>\n",
+       "      <td>5.975091</td>\n",
+       "      <td>0.171150</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
+       "      <td>0.469598</td>\n",
+       "      <td>0.474490</td>\n",
+       "      <td>0.473637</td>\n",
+       "      <td>0.472575</td>\n",
+       "      <td>0.002134</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>12.677349</td>\n",
+       "      <td>0.145143</td>\n",
+       "      <td>4.374204</td>\n",
+       "      <td>0.175674</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
+       "      <td>0.468872</td>\n",
+       "      <td>0.476451</td>\n",
+       "      <td>0.470921</td>\n",
+       "      <td>0.472082</td>\n",
+       "      <td>0.003201</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>13.423686</td>\n",
+       "      <td>0.482872</td>\n",
+       "      <td>8.008324</td>\n",
+       "      <td>0.442975</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
+       "      <td>0.465726</td>\n",
+       "      <td>0.474548</td>\n",
+       "      <td>0.471879</td>\n",
+       "      <td>0.470718</td>\n",
+       "      <td>0.003694</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13.819117</td>\n",
+       "      <td>0.838347</td>\n",
+       "      <td>6.161175</td>\n",
+       "      <td>0.336590</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>20</td>\n",
+       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
+       "      <td>0.463395</td>\n",
+       "      <td>0.473982</td>\n",
+       "      <td>0.471262</td>\n",
+       "      <td>0.469546</td>\n",
+       "      <td>0.004489</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>13.281476</td>\n",
+       "      <td>0.588822</td>\n",
+       "      <td>8.335852</td>\n",
+       "      <td>0.254627</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>20</td>\n",
+       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
+       "      <td>0.458734</td>\n",
+       "      <td>0.468053</td>\n",
+       "      <td>0.464418</td>\n",
+       "      <td>0.463735</td>\n",
+       "      <td>0.003835</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>7.282247</td>\n",
+       "      <td>0.444940</td>\n",
+       "      <td>3.567094</td>\n",
+       "      <td>0.044519</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
+       "      <td>0.438189</td>\n",
+       "      <td>0.450160</td>\n",
+       "      <td>0.446180</td>\n",
+       "      <td>0.444843</td>\n",
+       "      <td>0.004978</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>7.098797</td>\n",
+       "      <td>0.196241</td>\n",
+       "      <td>3.838628</td>\n",
+       "      <td>0.091128</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>100</td>\n",
+       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
+       "      <td>0.436488</td>\n",
+       "      <td>0.444503</td>\n",
+       "      <td>0.445900</td>\n",
+       "      <td>0.442297</td>\n",
+       "      <td>0.004147</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>7.492791</td>\n",
+       "      <td>0.288889</td>\n",
+       "      <td>3.843224</td>\n",
+       "      <td>0.073438</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>5</td>\n",
+       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
+       "      <td>0.428196</td>\n",
+       "      <td>0.431945</td>\n",
+       "      <td>0.430160</td>\n",
+       "      <td>0.430100</td>\n",
+       "      <td>0.001531</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>7.229826</td>\n",
+       "      <td>0.099823</td>\n",
+       "      <td>3.656332</td>\n",
+       "      <td>0.073780</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
+       "      <td>0.403130</td>\n",
+       "      <td>0.410170</td>\n",
+       "      <td>0.409801</td>\n",
+       "      <td>0.407700</td>\n",
+       "      <td>0.003235</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>7.158370</td>\n",
+       "      <td>0.169818</td>\n",
+       "      <td>3.765632</td>\n",
+       "      <td>0.082924</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>20</td>\n",
+       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
+       "      <td>0.399237</td>\n",
+       "      <td>0.412872</td>\n",
+       "      <td>0.407982</td>\n",
+       "      <td>0.406697</td>\n",
+       "      <td>0.005640</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>7.064643</td>\n",
+       "      <td>0.119529</td>\n",
+       "      <td>3.810983</td>\n",
+       "      <td>0.125897</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>20</td>\n",
+       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
+       "      <td>0.388060</td>\n",
+       "      <td>0.399247</td>\n",
+       "      <td>0.396325</td>\n",
+       "      <td>0.394544</td>\n",
+       "      <td>0.004737</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>13.749660</td>\n",
+       "      <td>0.465174</td>\n",
+       "      <td>6.407841</td>\n",
+       "      <td>0.549166</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>5</td>\n",
+       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
+       "      <td>0.384852</td>\n",
+       "      <td>0.386487</td>\n",
+       "      <td>0.386796</td>\n",
+       "      <td>0.386045</td>\n",
+       "      <td>0.000853</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>15.954147</td>\n",
+       "      <td>0.318013</td>\n",
+       "      <td>6.337361</td>\n",
+       "      <td>0.261697</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>\n",
+       "      <td>0.369785</td>\n",
+       "      <td>0.375645</td>\n",
+       "      <td>0.375858</td>\n",
+       "      <td>0.373763</td>\n",
+       "      <td>0.002814</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>7.120198</td>\n",
+       "      <td>0.050452</td>\n",
+       "      <td>3.833905</td>\n",
+       "      <td>0.069540</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>5</td>\n",
+       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
+       "      <td>0.277741</td>\n",
+       "      <td>0.280564</td>\n",
+       "      <td>0.285337</td>\n",
+       "      <td>0.281214</td>\n",
+       "      <td>0.003135</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>7.497707</td>\n",
+       "      <td>0.183054</td>\n",
+       "      <td>3.870714</td>\n",
+       "      <td>0.062888</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>\n",
+       "      <td>0.255578</td>\n",
+       "      <td>0.263381</td>\n",
+       "      <td>0.266184</td>\n",
+       "      <td>0.261714</td>\n",
+       "      <td>0.004487</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
+       "7       14.549476      0.361685         8.476837        0.222398   \n",
+       "10      11.235289      0.130426         4.092868        0.082518   \n",
+       "19       7.383645      0.138110         4.130709        0.250048   \n",
+       "11      10.435154      0.305144         3.882101        0.128886   \n",
+       "8       13.643193      0.310696         4.173707        0.142980   \n",
+       "22       7.048340      0.050070         3.172948        0.152418   \n",
+       "23       7.564685      0.146092         2.374111        0.285026   \n",
+       "20       7.172353      0.212599         3.747219        0.130217   \n",
+       "6       14.276345      0.456576         8.318859        0.268701   \n",
+       "2       14.902358      0.737693         5.975091        0.171150   \n",
+       "9       12.677349      0.145143         4.374204        0.175674   \n",
+       "5       13.423686      0.482872         8.008324        0.442975   \n",
+       "1       13.819117      0.838347         6.161175        0.336590   \n",
+       "4       13.281476      0.588822         8.335852        0.254627   \n",
+       "14       7.282247      0.444940         3.567094        0.044519   \n",
+       "17       7.098797      0.196241         3.838628        0.091128   \n",
+       "18       7.492791      0.288889         3.843224        0.073438   \n",
+       "21       7.229826      0.099823         3.656332        0.073780   \n",
+       "13       7.158370      0.169818         3.765632        0.082924   \n",
+       "16       7.064643      0.119529         3.810983        0.125897   \n",
+       "0       13.749660      0.465174         6.407841        0.549166   \n",
+       "3       15.954147      0.318013         6.337361        0.261697   \n",
+       "12       7.120198      0.050452         3.833905        0.069540   \n",
+       "15       7.497707      0.183054         3.870714        0.062888   \n",
+       "\n",
+       "   param_classifier__alpha param_classifier__fit_prior  \\\n",
+       "7                      0.5                       False   \n",
+       "10                     0.5                       False   \n",
+       "19                       1                       False   \n",
+       "11                     0.5                       False   \n",
+       "8                      0.5                       False   \n",
+       "22                       1                       False   \n",
+       "23                       1                       False   \n",
+       "20                       1                       False   \n",
+       "6                      0.5                       False   \n",
+       "2                      0.5                        True   \n",
+       "9                      0.5                       False   \n",
+       "5                      0.5                        True   \n",
+       "1                      0.5                        True   \n",
+       "4                      0.5                        True   \n",
+       "14                       1                        True   \n",
+       "17                       1                        True   \n",
+       "18                       1                       False   \n",
+       "21                       1                       False   \n",
+       "13                       1                        True   \n",
+       "16                       1                        True   \n",
+       "0                      0.5                        True   \n",
+       "3                      0.5                        True   \n",
+       "12                       1                        True   \n",
+       "15                       1                        True   \n",
+       "\n",
+       "   param_vectorizer__max_df param_vectorizer__min_df  \\\n",
+       "7                      0.05                       20   \n",
+       "10                      0.1                       20   \n",
+       "19                     0.05                       20   \n",
+       "11                      0.1                      100   \n",
+       "8                      0.05                      100   \n",
+       "22                      0.1                       20   \n",
+       "23                      0.1                      100   \n",
+       "20                     0.05                      100   \n",
+       "6                      0.05                        5   \n",
+       "2                      0.05                      100   \n",
+       "9                       0.1                        5   \n",
+       "5                       0.1                      100   \n",
+       "1                      0.05                       20   \n",
+       "4                       0.1                       20   \n",
+       "14                     0.05                      100   \n",
+       "17                      0.1                      100   \n",
+       "18                     0.05                        5   \n",
+       "21                      0.1                        5   \n",
+       "13                     0.05                       20   \n",
+       "16                      0.1                       20   \n",
+       "0                      0.05                        5   \n",
+       "3                       0.1                        5   \n",
+       "12                     0.05                        5   \n",
+       "15                      0.1                        5   \n",
+       "\n",
+       "                                               params  split0_test_score  \\\n",
+       "7   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.518061   \n",
+       "10  {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.513897   \n",
+       "19  {'classifier__alpha': 1, 'classifier__fit_prio...           0.496825   \n",
+       "11  {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.493247   \n",
+       "8   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.489609   \n",
+       "22  {'classifier__alpha': 1, 'classifier__fit_prio...           0.487456   \n",
+       "23  {'classifier__alpha': 1, 'classifier__fit_prio...           0.485160   \n",
+       "20  {'classifier__alpha': 1, 'classifier__fit_prio...           0.481303   \n",
+       "6   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.482429   \n",
+       "2   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.469598   \n",
+       "9   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.468872   \n",
+       "5   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.465726   \n",
+       "1   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.463395   \n",
+       "4   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.458734   \n",
+       "14  {'classifier__alpha': 1, 'classifier__fit_prio...           0.438189   \n",
+       "17  {'classifier__alpha': 1, 'classifier__fit_prio...           0.436488   \n",
+       "18  {'classifier__alpha': 1, 'classifier__fit_prio...           0.428196   \n",
+       "21  {'classifier__alpha': 1, 'classifier__fit_prio...           0.403130   \n",
+       "13  {'classifier__alpha': 1, 'classifier__fit_prio...           0.399237   \n",
+       "16  {'classifier__alpha': 1, 'classifier__fit_prio...           0.388060   \n",
+       "0   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.384852   \n",
+       "3   {'classifier__alpha': 0.5, 'classifier__fit_pr...           0.369785   \n",
+       "12  {'classifier__alpha': 1, 'classifier__fit_prio...           0.277741   \n",
+       "15  {'classifier__alpha': 1, 'classifier__fit_prio...           0.255578   \n",
+       "\n",
+       "    split1_test_score  split2_test_score  mean_test_score  std_test_score  \\\n",
+       "7            0.514842           0.511599         0.514834        0.002638   \n",
+       "10           0.515661           0.507867         0.512475        0.003337   \n",
+       "19           0.501045           0.496854         0.498241        0.001983   \n",
+       "11           0.497814           0.502245         0.497769        0.003674   \n",
+       "8            0.495207           0.498154         0.494323        0.003544   \n",
+       "22           0.493865           0.491157         0.490826        0.002627   \n",
+       "23           0.494039           0.490127         0.489776        0.003633   \n",
+       "20           0.490002           0.488269         0.486524        0.003759   \n",
+       "6            0.487735           0.484888         0.485017        0.002168   \n",
+       "2            0.474490           0.473637         0.472575        0.002134   \n",
+       "9            0.476451           0.470921         0.472082        0.003201   \n",
+       "5            0.474548           0.471879         0.470718        0.003694   \n",
+       "1            0.473982           0.471262         0.469546        0.004489   \n",
+       "4            0.468053           0.464418         0.463735        0.003835   \n",
+       "14           0.450160           0.446180         0.444843        0.004978   \n",
+       "17           0.444503           0.445900         0.442297        0.004147   \n",
+       "18           0.431945           0.430160         0.430100        0.001531   \n",
+       "21           0.410170           0.409801         0.407700        0.003235   \n",
+       "13           0.412872           0.407982         0.406697        0.005640   \n",
+       "16           0.399247           0.396325         0.394544        0.004737   \n",
+       "0            0.386487           0.386796         0.386045        0.000853   \n",
+       "3            0.375645           0.375858         0.373763        0.002814   \n",
+       "12           0.280564           0.285337         0.281214        0.003135   \n",
+       "15           0.263381           0.266184         0.261714        0.004487   \n",
+       "\n",
+       "    rank_test_score  \n",
+       "7                 1  \n",
+       "10                2  \n",
+       "19                3  \n",
+       "11                4  \n",
+       "8                 5  \n",
+       "22                6  \n",
+       "23                7  \n",
+       "20                8  \n",
+       "6                 9  \n",
+       "2                10  \n",
+       "9                11  \n",
+       "5                12  \n",
+       "1                13  \n",
+       "4                14  \n",
+       "14               15  \n",
+       "17               16  \n",
+       "18               17  \n",
+       "21               18  \n",
+       "13               19  \n",
+       "16               20  \n",
+       "0                21  \n",
+       "3                22  \n",
+       "12               23  \n",
+       "15               24  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "from sklearn.model_selection import GridSearchCV\n",
+    "import pandas as pd\n",
     "\n",
     "optimisation_pipeline = GridSearchCV(\n",
     "    create_pipeline(),\n",
@@ -157,7 +784,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -199,7 +826,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -207,15 +834,15 @@
      "output_type": "stream",
      "text": [
       "\u001b[38;5;39mFetching cached versions of small-domain-prediction\u001b[0m\n",
-      "\u001b[38;5;39mCopying file for small-domain-prediction-1\u001b[0m\n",
-      "\u001b[38;5;39mCompressing small-domain-prediction-1\u001b[0m\n",
-      "\u001b[38;5;39mModel small-domain-prediction uploaded with version 1\u001b[0m\n"
+      "\u001b[38;5;39mCopying file for small-domain-prediction-2\u001b[0m\n",
+      "\u001b[38;5;39mCompressing small-domain-prediction-2\u001b[0m\n",
+      "\u001b[38;5;39mModel small-domain-prediction uploaded with version 2\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "'small-domain-prediction:1'"
+       "'small-domain-prediction:2'"
       ]
      },
      "execution_count": 6,
diff --git a/search/search_index.json b/search/search_index.json
index 15bba94..a0419dd 100644
--- a/search/search_index.json
+++ b/search/search_index.json
@@ -1 +1 @@
-{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"Overview of GreatAI Applying AI is becoming increasingly easier but many case studies have shown that these applications are often deployed poorly. This may lead to suboptimal performance and to introducing unintended biases . GreatAI helps fixing this by allowing you to easily transform your prototype AI code into production-ready software . Case studies \"There is a need to consider and adapt well established SE practices which have been ignored or had a very narrow focus in ML literature.\" \u2014 John et al. \"Finally, we have found that existing tools to aid Machine Learning development do not address the specificities of different projects, and thus, are seldom adopted by teams.\" \u2014 Haakman et al. \"Because a mature system might end up being (at most) 5% machine learning code and (at least) 95% glue code, it may be less costly to create a clean native solution rather than re-use a generic package.\" \u2014 Sculley et al. \"For example, practice 25 is very important for \u201cTraceability\", yet relatively weakly adopted. We expect that the results from this type of analysis can, in the future, provide useful guidance for practitioners in terms of aiding them to assess their rate of adoption for each practice and to create roadmaps for improving their processes. \u2014 Serban et al. Features # Save prediction traces of each prediction including arguments and model versions Save feedback and merge it into a ground-truth database Version and store models and data on shared infrastructure (MongoDB GridFS, S3-compatible storage, shared local-volume) Automatically scaffolded custom REST API (and OpenAPI schema) for easy integration Input validation Sensible cache-policy Seamless support for both synchronous and asynchronous inference methods Easy integration with remote GreatAI instances Built-in parallelisation (with support for multiprocessing, async, and mixed modes) for batch processing Well-tested utilities for common NLP tasks (cleaning, language-tagging, sentence-segmentation, etc.) A simple, unified configuration interface Fully-typed API for Pylance and MyPy support Auto-reload for development Docker support for deployment Deployable Jupyter Notebooks Dashboard for high-level overview and analysing traces Roadmap # Prometheus & Grafana integration Well-tested feature extraction code for non-NLP data Support for direct file input Support for PostgreSQL Hello world # pip install great-ai demo.py from great_ai import GreatAI @GreatAI . create #(1) def greeter ( name : str ) -> str : #(2) return f \"Hello { name } !\" @GreatAI.create wraps your greeter function with a GreatAI instance. The function will behave very similarly but: its return value becomes a Trace[str] , it gets a process_batch method for supporting parallel execution, and it can be deployed using the great-ai command-line tool. Typing functions is recommended in general, however, not required for GreatAI to work. Note In practice, greeter could be an inference function of some AI/ML application. But it could also just wrap a black-box solution of some SaaS. Either way, it is imperative to have continuous oversight of the services you provide and data you process especially in the context of AI/ML applications. terminal great-ai demo.py Navigate to localhost:6060 in your browser. Success Your GreatAI service is ready for production use. Many of the SE4ML best-practices are configured and implemented automatically. To have full control over your service and to understand what else you might need to do in your use case, continue reading this documentation. Why is this GREAT? # GreatAI fits between the prototype and deployment phases of your (or your organisation's) AI development lifecycle. This is highlighted with blue in the diagram. Here, several best practices can be automatically implemented aiming to achieve the following attributes: G eneral: use any Python library without restriction R obust: have error-handling and well-tested utilities out-of-the-box E nd-to-end: utilise end-to-end feedback as a built-in, first-class concept A utomated: focus only on what actually requires your attention T rustworthy: deploy models that you and society can confidently trust Why GreatAI? # There are other, existing solutions aiming to facilitate this phase. Amazon SageMaker and Seldon Core provide the most comprehensive suite of features. If you have the opportunity to use them, do that because they're great. However, research indicates that professionals rarely use them. This may be due to their inherent setup and operating complexity. GreatAI is designed to be as simple to use as possible. Its clear, high-level API and sensible default configuration makes it extremely easy to start using. Despite its relative simplicity over Seldon Core, it still implements many of the SE4ML best-practices , and thus, can meaningfully improve your deployment without requiring prohibitively large effort. Find it on PyPI Find it on DockerHub Check out the tutorial Production use # GreatAI has been battle-tested on the core platform services of ScoutinScience .","title":"Overview"},{"location":"#features","text":"Save prediction traces of each prediction including arguments and model versions Save feedback and merge it into a ground-truth database Version and store models and data on shared infrastructure (MongoDB GridFS, S3-compatible storage, shared local-volume) Automatically scaffolded custom REST API (and OpenAPI schema) for easy integration Input validation Sensible cache-policy Seamless support for both synchronous and asynchronous inference methods Easy integration with remote GreatAI instances Built-in parallelisation (with support for multiprocessing, async, and mixed modes) for batch processing Well-tested utilities for common NLP tasks (cleaning, language-tagging, sentence-segmentation, etc.) A simple, unified configuration interface Fully-typed API for Pylance and MyPy support Auto-reload for development Docker support for deployment Deployable Jupyter Notebooks Dashboard for high-level overview and analysing traces","title":"Features"},{"location":"#roadmap","text":"Prometheus & Grafana integration Well-tested feature extraction code for non-NLP data Support for direct file input Support for PostgreSQL","title":"Roadmap"},{"location":"#hello-world","text":"pip install great-ai demo.py from great_ai import GreatAI @GreatAI . create #(1) def greeter ( name : str ) -> str : #(2) return f \"Hello { name } !\" @GreatAI.create wraps your greeter function with a GreatAI instance. The function will behave very similarly but: its return value becomes a Trace[str] , it gets a process_batch method for supporting parallel execution, and it can be deployed using the great-ai command-line tool. Typing functions is recommended in general, however, not required for GreatAI to work. Note In practice, greeter could be an inference function of some AI/ML application. But it could also just wrap a black-box solution of some SaaS. Either way, it is imperative to have continuous oversight of the services you provide and data you process especially in the context of AI/ML applications. terminal great-ai demo.py Navigate to localhost:6060 in your browser. Success Your GreatAI service is ready for production use. Many of the SE4ML best-practices are configured and implemented automatically. To have full control over your service and to understand what else you might need to do in your use case, continue reading this documentation.","title":"Hello world"},{"location":"#why-is-this-great","text":"GreatAI fits between the prototype and deployment phases of your (or your organisation's) AI development lifecycle. This is highlighted with blue in the diagram. Here, several best practices can be automatically implemented aiming to achieve the following attributes: G eneral: use any Python library without restriction R obust: have error-handling and well-tested utilities out-of-the-box E nd-to-end: utilise end-to-end feedback as a built-in, first-class concept A utomated: focus only on what actually requires your attention T rustworthy: deploy models that you and society can confidently trust","title":"Why is this GREAT?"},{"location":"#why-greatai","text":"There are other, existing solutions aiming to facilitate this phase. Amazon SageMaker and Seldon Core provide the most comprehensive suite of features. If you have the opportunity to use them, do that because they're great. However, research indicates that professionals rarely use them. This may be due to their inherent setup and operating complexity. GreatAI is designed to be as simple to use as possible. Its clear, high-level API and sensible default configuration makes it extremely easy to start using. Despite its relative simplicity over Seldon Core, it still implements many of the SE4ML best-practices , and thus, can meaningfully improve your deployment without requiring prohibitively large effort. Find it on PyPI Find it on DockerHub Check out the tutorial","title":"Why GreatAI?"},{"location":"#production-use","text":"GreatAI has been battle-tested on the core platform services of ScoutinScience .","title":"Production use"},{"location":"explanation/","text":"Explanation # A lot more details and discussion about the problem context and approaches of GreatAI along with its evaluation can be found in my thesis. : Download (available soon)","title":"Explanation"},{"location":"explanation/#explanation","text":"A lot more details and discussion about the problem context and approaches of GreatAI along with its evaluation can be found in my thesis. : Download (available soon)","title":"Explanation"},{"location":"examples/scibert/","text":"Summarising scientific publications from a tech-transfer perspective # This example shows how great-ai is used in practice at ScoutinScience. Check out the code","title":"Explainable SciBERT"},{"location":"examples/scibert/#summarising-scientific-publications-from-a-tech-transfer-perspective","text":"This example shows how great-ai is used in practice at ScoutinScience. Check out the code","title":"Summarising scientific publications from a tech-transfer perspective"},{"location":"examples/scibert/views/__init__/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. 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var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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selection.addRange(range); document.execCommand('copy'); selection.removeAllRanges(); return Promise.resolve(); } function copyText(text) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(text); } const body = document.body; if (!body) { return Promise.reject(new Error()); } const node = createNode(text); body.appendChild(node); copyNode(node); body.removeChild(node); return Promise.resolve(); } function copy(button) { const id = button.getAttribute('for'); const text = button.getAttribute('value'); function trigger() { button.dispatchEvent(new CustomEvent('clipboard-copy', { bubbles: true })); } if (text) { copyText(text).then(trigger); } else if (id) { const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument; if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return; const node = root.getElementById(id); if (node) copyTarget(node).then(trigger); } } function copyTarget(content) { if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) { return copyText(content.value); } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) { return copyText(content.href); } else { return copyNode(content); } } function clicked(event) { const button = event.currentTarget; if (button instanceof HTMLElement) { copy(button); } } function keydown(event) { if (event.key === ' ' || event.key === 'Enter') { const button = event.currentTarget; if (button instanceof HTMLElement) { event.preventDefault(); copy(button); } } } function focused(event) { event.currentTarget.addEventListener('keydown', keydown); } function blurred(event) { event.currentTarget.removeEventListener('keydown', keydown); } class ClipboardCopyElement extends HTMLElement { constructor() { super(); this.addEventListener('click', clicked); 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} /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); const range = document.createRange(); range.selectNodeContents(node); selection.addRange(range); document.execCommand('copy'); selection.removeAllRanges(); return Promise.resolve(); } function copyText(text) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(text); } const body = document.body; if (!body) { return Promise.reject(new Error()); } const node = createNode(text); body.appendChild(node); copyNode(node); body.removeChild(node); return Promise.resolve(); } function copy(button) { const id = button.getAttribute('for'); const text = button.getAttribute('value'); function trigger() { button.dispatchEvent(new CustomEvent('clipboard-copy', { bubbles: true })); } if (text) { copyText(text).then(trigger); } else if (id) { const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument; if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return; const node = root.getElementById(id); if (node) copyTarget(node).then(trigger); } } function copyTarget(content) { if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) { return copyText(content.value); } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) { return copyText(content.href); } else { return copyNode(content); } } function clicked(event) { const button = event.currentTarget; if (button instanceof HTMLElement) { copy(button); } } function keydown(event) { if (event.key === ' ' || event.key === 'Enter') { const button = event.currentTarget; if (button instanceof HTMLElement) { event.preventDefault(); copy(button); } } } function focused(event) { event.currentTarget.addEventListener('keydown', keydown); } function blurred(event) { event.currentTarget.removeEventListener('keydown', keydown); } class ClipboardCopyElement extends HTMLElement { constructor() { super(); this.addEventListener('click', clicked); this.addEventListener('focus', focused); this.addEventListener('blur', blurred); } connectedCallback() { if (!this.hasAttribute('tabindex')) { this.setAttribute('tabindex', '0'); } if (!this.hasAttribute('role')) { this.setAttribute('role', 'button'); } } get value() { return 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var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Simple example: data engineering \u00b6 Here, we solve a problem similar to the tutorial's but with an explainable Naive Bayes classifier and more best-practices. In short, we train a domain classifier on the semantic scholar dataset by taking full advantage of great-ai . Subsequently, we create a production-ready deployment. The blue boxes show the steps of a typical AI-development lifecycle implemented in this notebook. Since the true scope of great-ai is the phase between proof-of-concept code and production-ready service, it is predominantly used in the deployment notebook . Feel free to skip there, or continue reading if you'd like to see the full picture. Extract \u00b6 This can be achieved by downloading a public dataset (such as in this case), or by having a Data Engineer setup and give us access to the organisation's data. In this example, we download the semantic scholar dataset from a public S3 bucket. In [1]: Copied! MAX_CHUNK_COUNT = 4 MAX_CHUNK_COUNT = 4 In [2]: Copied! import urllib.request from random import shuffle manifest = ( urllib . request . urlopen ( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"open-corpus/2022-02-01/manifest.txt\" ) . read () . decode () ) # a list of available chunks separated by '\\n' characters lines = manifest . split () shuffle ( lines ) chunks = lines [: MAX_CHUNK_COUNT ] f \"\"\"Processing { len ( chunks ) } out of the { len ( manifest . split ()) } available chunks\"\"\" import urllib.request from random import shuffle manifest = ( urllib.request.urlopen( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"open-corpus/2022-02-01/manifest.txt\" ) .read() .decode() ) # a list of available chunks separated by '\\n' characters lines = manifest.split() shuffle(lines) chunks = lines[:MAX_CHUNK_COUNT] f\"\"\"Processing {len(chunks)} out of the { len(manifest.split()) } available chunks\"\"\" Out[2]: 'Processing 4 out of the 6002 available chunks' Transform \u00b6 Filter out non-English abstracts using great_ai.utilities.predict_language Project it to only keep the necessary components (text and labels), clean the textual content using great_ai.utilities.clean We will speed up processing using great_ai.utilities.simple_parallel_map . In [3]: Copied! from typing import List , Tuple import json import gzip from great_ai.utilities import ( simple_parallel_map , clean , is_english , predict_language , unchunk , ) def preprocess_chunk ( chunk_key : str ) -> List [ Tuple [ str , List [ str ]]]: response = urllib . request . urlopen ( f \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" f \"open-corpus/2022-02-01/ { chunk_key } \" ) # a gzipped JSON Lines file decompressed = gzip . decompress ( response . read ()) decoded = decompressed . decode () chunk = [ json . loads ( line ) for line in decoded . split ( \" \\n \" ) if line ] # Transform return [ ( clean ( f ' { c [ \"title\" ] } { c [ \"paperAbstract\" ] } ' f ' { c [ \"journalName\" ] } { c [ \"venue\" ] } ' , convert_to_ascii = True , ), # The text is cleaned to remove common artifacts c [ \"fieldsOfStudy\" ], ) # Create pairs of `(text, [...domains])` for c in chunk if ( c [ \"fieldsOfStudy\" ] and is_english ( predict_language ( c [ \"paperAbstract\" ]))) ] preprocessed_data = unchunk ( simple_parallel_map ( preprocess_chunk , chunks , concurrency = 4 ) ) from typing import List, Tuple import json import gzip from great_ai.utilities import ( simple_parallel_map, clean, is_english, predict_language, unchunk, ) def preprocess_chunk(chunk_key: str) -> List[Tuple[str, List[str]]]: response = urllib.request.urlopen( f\"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" f\"open-corpus/2022-02-01/{chunk_key}\" ) # a gzipped JSON Lines file decompressed = gzip.decompress(response.read()) decoded = decompressed.decode() chunk = [json.loads(line) for line in decoded.split(\"\\n\") if line] # Transform return [ ( clean( f'{c[\"title\"]} {c[\"paperAbstract\"]} ' f'{c[\"journalName\"]} {c[\"venue\"]}', convert_to_ascii=True, ), # The text is cleaned to remove common artifacts c[\"fieldsOfStudy\"], ) # Create pairs of `(text, [...domains])` for c in chunk if (c[\"fieldsOfStudy\"] and is_english(predict_language(c[\"paperAbstract\"]))) ] preprocessed_data = unchunk( simple_parallel_map(preprocess_chunk, chunks, concurrency=4) ) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4/4 [04:22<00:00, 65.51s/it] In [4]: Copied! X , y = zip ( * preprocessed_data ) # X is the input, y is the expected output X, y = zip(*preprocessed_data) # X is the input, y is the expected output Load \u00b6 Upload the dataset (or a part of it) to a central repository using great_ai.add_ground_truth . This step automatically tags each data-point with a split label according to the ratios we set. Additional tags can be also given. Production-ready backend \u00b6 The MongoDB driver is automatically configured if mongo.ini exists with the following scheme: mongo_connection_string = mongodb://localhost:27017/ mongo_database = my_great_ai_db You can install MongoDB from here or use it as a service Otherwise, TinyDB is used which is just a local JSON file. In [5]: Copied! from great_ai import add_ground_truth add_ground_truth ( X , y , train_split_ratio = 0.8 , test_split_ratio = 0.2 ) from great_ai import add_ground_truth add_ground_truth(X, y, train_split_ratio=0.8, test_split_ratio=0.2) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Next: Part 2 \u00b6","title":"Simple example: data engineering"},{"location":"examples/simple/data/#simple-example-data-engineering","text":"Here, we solve a problem similar to the tutorial's but with an explainable Naive Bayes classifier and more best-practices. In short, we train a domain classifier on the semantic scholar dataset by taking full advantage of great-ai . Subsequently, we create a production-ready deployment. The blue boxes show the steps of a typical AI-development lifecycle implemented in this notebook. Since the true scope of great-ai is the phase between proof-of-concept code and production-ready service, it is predominantly used in the deployment notebook . Feel free to skip there, or continue reading if you'd like to see the full picture.","title":"Simple example: data engineering"},{"location":"examples/simple/data/#extract","text":"This can be achieved by downloading a public dataset (such as in this case), or by having a Data Engineer setup and give us access to the organisation's data. In this example, we download the semantic scholar dataset from a public S3 bucket. In [1]: Copied! MAX_CHUNK_COUNT = 4 MAX_CHUNK_COUNT = 4 In [2]: Copied! import urllib.request from random import shuffle manifest = ( urllib . request . urlopen ( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"open-corpus/2022-02-01/manifest.txt\" ) . read () . decode () ) # a list of available chunks separated by '\\n' characters lines = manifest . split () shuffle ( lines ) chunks = lines [: MAX_CHUNK_COUNT ] f \"\"\"Processing { len ( chunks ) } out of the { len ( manifest . split ()) } available chunks\"\"\" import urllib.request from random import shuffle manifest = ( urllib.request.urlopen( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"open-corpus/2022-02-01/manifest.txt\" ) .read() .decode() ) # a list of available chunks separated by '\\n' characters lines = manifest.split() shuffle(lines) chunks = lines[:MAX_CHUNK_COUNT] f\"\"\"Processing {len(chunks)} out of the { len(manifest.split()) } available chunks\"\"\" Out[2]: 'Processing 4 out of the 6002 available chunks'","title":"Extract"},{"location":"examples/simple/data/#transform","text":"Filter out non-English abstracts using great_ai.utilities.predict_language Project it to only keep the necessary components (text and labels), clean the textual content using great_ai.utilities.clean We will speed up processing using great_ai.utilities.simple_parallel_map . In [3]: Copied! from typing import List , Tuple import json import gzip from great_ai.utilities import ( simple_parallel_map , clean , is_english , predict_language , unchunk , ) def preprocess_chunk ( chunk_key : str ) -> List [ Tuple [ str , List [ str ]]]: response = urllib . request . urlopen ( f \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" f \"open-corpus/2022-02-01/ { chunk_key } \" ) # a gzipped JSON Lines file decompressed = gzip . decompress ( response . read ()) decoded = decompressed . decode () chunk = [ json . loads ( line ) for line in decoded . split ( \" \\n \" ) if line ] # Transform return [ ( clean ( f ' { c [ \"title\" ] } { c [ \"paperAbstract\" ] } ' f ' { c [ \"journalName\" ] } { c [ \"venue\" ] } ' , convert_to_ascii = True , ), # The text is cleaned to remove common artifacts c [ \"fieldsOfStudy\" ], ) # Create pairs of `(text, [...domains])` for c in chunk if ( c [ \"fieldsOfStudy\" ] and is_english ( predict_language ( c [ \"paperAbstract\" ]))) ] preprocessed_data = unchunk ( simple_parallel_map ( preprocess_chunk , chunks , concurrency = 4 ) ) from typing import List, Tuple import json import gzip from great_ai.utilities import ( simple_parallel_map, clean, is_english, predict_language, unchunk, ) def preprocess_chunk(chunk_key: str) -> List[Tuple[str, List[str]]]: response = urllib.request.urlopen( f\"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" f\"open-corpus/2022-02-01/{chunk_key}\" ) # a gzipped JSON Lines file decompressed = gzip.decompress(response.read()) decoded = decompressed.decode() chunk = [json.loads(line) for line in decoded.split(\"\\n\") if line] # Transform return [ ( clean( f'{c[\"title\"]} {c[\"paperAbstract\"]} ' f'{c[\"journalName\"]} {c[\"venue\"]}', convert_to_ascii=True, ), # The text is cleaned to remove common artifacts c[\"fieldsOfStudy\"], ) # Create pairs of `(text, [...domains])` for c in chunk if (c[\"fieldsOfStudy\"] and is_english(predict_language(c[\"paperAbstract\"]))) ] preprocessed_data = unchunk( simple_parallel_map(preprocess_chunk, chunks, concurrency=4) ) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4/4 [04:22<00:00, 65.51s/it] In [4]: Copied! X , y = zip ( * preprocessed_data ) # X is the input, y is the expected output X, y = zip(*preprocessed_data) # X is the input, y is the expected output","title":"Transform"},{"location":"examples/simple/data/#load","text":"Upload the dataset (or a part of it) to a central repository using great_ai.add_ground_truth . This step automatically tags each data-point with a split label according to the ratios we set. Additional tags can be also given.","title":"Load"},{"location":"examples/simple/data/#production-ready-backend","text":"The MongoDB driver is automatically configured if mongo.ini exists with the following scheme: mongo_connection_string = mongodb://localhost:27017/ mongo_database = my_great_ai_db You can install MongoDB from here or use it as a service Otherwise, TinyDB is used which is just a local JSON file. In [5]: Copied! from great_ai import add_ground_truth add_ground_truth ( X , y , train_split_ratio = 0.8 , test_split_ratio = 0.2 ) from great_ai import add_ground_truth add_ground_truth(X, y, train_split_ratio=0.8, test_split_ratio=0.2) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices","title":"Production-ready backend"},{"location":"examples/simple/data/#next-part-2","text":"","title":"Next: Part 2"},{"location":"examples/simple/deploy/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Hardening and deployment \u00b6 Part 3: Create production inference function The blue boxes show the steps implemented in this notebook. In Part 2 , we trained our AI model. Now, it's time to create G eneral R obust E nd-to-end A utomated T rustworthy deployment. Create the inference function \u00b6 Next to the prediction, we also return the top-n most influential words based on their weights. In [1]: Copied! import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput , ClassificationOutput , GreatAI , use_model , parameter , ) @GreatAI . create @use_model ( \"small-domain-prediction\" , version = \"latest\" ) @parameter ( \"target_confidence\" , validate = lambda c : 0 <= c <= 100 ) def predict_domain ( text : str , model : Pipeline , target_confidence : int = 50 ) -> MultiLabelClassificationOutput : \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re . sub ( r \"[^a-zA-Z\\s]\" , \"\" , clean ( text , convert_to_ascii = True )) features = model . named_steps [ \"vectorizer\" ] . transform ([ preprocessed ]) prediction = model . named_steps [ \"classifier\" ] . predict_proba ( features )[ 0 ] best_classes = sorted ( enumerate ( prediction ), key = lambda v : v [ 1 ], reverse = True ) results = MultiLabelClassificationOutput () for class_index , probability in best_classes : results . labels . append ( get_label ( model = model , features = features , class_index = class_index , probability = probability , ) ) if sum ( r . confidence for r in results . labels ) >= target_confidence : break return results def get_label ( model : Pipeline , features : np . ndarray , class_index : int , probability : float ) -> ClassificationOutput : return ClassificationOutput ( label = model . named_steps [ \"classifier\" ] . classes_ [ class_index ], confidence = round ( probability * 100 ), explanation = [ word for _ , word in sorted ( ( ( weight , word ) for weight , word , count in zip ( model . named_steps [ \"classifier\" ] . feature_log_prob_ [ class_index ], model . named_steps [ \"vectorizer\" ] . get_feature_names_out (), features . A [ 0 ], ) if count > 0 ), reverse = True , ) ][: 5 ], ) import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput, ClassificationOutput, GreatAI, use_model, parameter, ) @GreatAI.create @use_model(\"small-domain-prediction\", version=\"latest\") @parameter(\"target_confidence\", validate=lambda c: 0 < = c < = 100) def predict_domain( text: str, model: Pipeline, target_confidence: int = 50 ) -> MultiLabelClassificationOutput: \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re.sub(r\"[^a-zA-Z\\s]\", \"\", clean(text, convert_to_ascii=True)) features = model.named_steps[\"vectorizer\"].transform([preprocessed]) prediction = model.named_steps[\"classifier\"].predict_proba(features)[0] best_classes = sorted(enumerate(prediction), key=lambda v: v[1], reverse=True) results = MultiLabelClassificationOutput() for class_index, probability in best_classes: results.labels.append( get_label( model=model, features=features, class_index=class_index, probability=probability, ) ) if sum(r.confidence for r in results.labels) >= target_confidence: break return results def get_label( model: Pipeline, features: np.ndarray, class_index: int, probability: float ) -> ClassificationOutput: return ClassificationOutput( label=model.named_steps[\"classifier\"].classes_[class_index], confidence=round(probability * 100), explanation=[ word for _, word in sorted( ( (weight, word) for weight, word, count in zip( model.named_steps[\"classifier\"].feature_log_prob_[class_index], model.named_steps[\"vectorizer\"].get_feature_names_out(), features.A[0], ) if count > 0 ), reverse=True, ) ][:5], ) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.4): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of small-domain-prediction Latest version of small-domain-prediction is 0 (from versions: 0) File small-domain-prediction-0 found in cache Check accuracy on the test split \u00b6 Anything under if __name__ == \"__main__\": will not be run when the script is executed by the great-ai CLI app. This, combined with query_ground_truth and the /traces/{trace_id}/feedback endpoint are ideal for creating a continuous-integration job for checking the quality of the model before deployment. In [2]: Copied! if __name__ == \"__main__\" : from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth ( \"test\" ) X = [ d . input for d in data ] y_actual = [ d . feedback for d in data ] y_predicted = [ d . output . labels [ 0 ] . label for d in predict_domain . process_batch ( X , do_not_persist_traces = True ) ] y_actual_aligned = [ p if p in a else a [ 0 ] for p , a in zip ( y_predicted , y_actual )] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_actual_aligned , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_actual_aligned , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) if __name__ == \"__main__\": from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth(\"test\") X = [d.input for d in data] y_actual = [d.feedback for d in data] y_predicted = [ d.output.labels[0].label for d in predict_domain.process_batch(X, do_not_persist_traces=True) ] y_actual_aligned = [p if p in a else a[0] for p, a in zip(y_predicted, y_actual)] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_actual_aligned, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_actual_aligned, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 12332/12332 [01:27<00:00, 141.08it/s] precision recall f1-score support Art 0.43 0.35 0.39 125 Biology 0.77 0.83 0.80 1209 Business 0.50 0.71 0.59 312 Chemistry 0.80 0.67 0.73 1194 Computer Science 0.77 0.76 0.77 1293 Economics 0.64 0.58 0.61 251 Engineering 0.55 0.53 0.54 815 Environmental Science 0.54 0.56 0.55 230 Geography 0.54 0.44 0.48 277 Geology 0.76 0.67 0.71 228 History 0.29 0.21 0.24 102 Materials Science 0.73 0.81 0.77 1053 Mathematics 0.80 0.70 0.75 551 Medicine 0.95 0.77 0.85 2794 Philosophy 0.61 0.12 0.21 88 Physics 0.67 0.76 0.72 605 Political Science 0.43 0.52 0.47 297 Psychology 0.53 0.80 0.63 600 Sociology 0.32 0.60 0.42 308 accuracy 0.71 12332 macro avg 0.61 0.60 0.59 12332 weighted avg 0.73 0.71 0.71 12332","title":"Hardening and deployment"},{"location":"examples/simple/deploy/#hardening-and-deployment","text":"Part 3: Create production inference function The blue boxes show the steps implemented in this notebook. In Part 2 , we trained our AI model. Now, it's time to create G eneral R obust E nd-to-end A utomated T rustworthy deployment.","title":"Hardening and deployment"},{"location":"examples/simple/deploy/#create-the-inference-function","text":"Next to the prediction, we also return the top-n most influential words based on their weights. In [1]: Copied! import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput , ClassificationOutput , GreatAI , use_model , parameter , ) @GreatAI . create @use_model ( \"small-domain-prediction\" , version = \"latest\" ) @parameter ( \"target_confidence\" , validate = lambda c : 0 <= c <= 100 ) def predict_domain ( text : str , model : Pipeline , target_confidence : int = 50 ) -> MultiLabelClassificationOutput : \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re . sub ( r \"[^a-zA-Z\\s]\" , \"\" , clean ( text , convert_to_ascii = True )) features = model . named_steps [ \"vectorizer\" ] . transform ([ preprocessed ]) prediction = model . named_steps [ \"classifier\" ] . predict_proba ( features )[ 0 ] best_classes = sorted ( enumerate ( prediction ), key = lambda v : v [ 1 ], reverse = True ) results = MultiLabelClassificationOutput () for class_index , probability in best_classes : results . labels . append ( get_label ( model = model , features = features , class_index = class_index , probability = probability , ) ) if sum ( r . confidence for r in results . labels ) >= target_confidence : break return results def get_label ( model : Pipeline , features : np . ndarray , class_index : int , probability : float ) -> ClassificationOutput : return ClassificationOutput ( label = model . named_steps [ \"classifier\" ] . classes_ [ class_index ], confidence = round ( probability * 100 ), explanation = [ word for _ , word in sorted ( ( ( weight , word ) for weight , word , count in zip ( model . named_steps [ \"classifier\" ] . feature_log_prob_ [ class_index ], model . named_steps [ \"vectorizer\" ] . get_feature_names_out (), features . A [ 0 ], ) if count > 0 ), reverse = True , ) ][: 5 ], ) import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput, ClassificationOutput, GreatAI, use_model, parameter, ) @GreatAI.create @use_model(\"small-domain-prediction\", version=\"latest\") @parameter(\"target_confidence\", validate=lambda c: 0 < = c < = 100) def predict_domain( text: str, model: Pipeline, target_confidence: int = 50 ) -> MultiLabelClassificationOutput: \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re.sub(r\"[^a-zA-Z\\s]\", \"\", clean(text, convert_to_ascii=True)) features = model.named_steps[\"vectorizer\"].transform([preprocessed]) prediction = model.named_steps[\"classifier\"].predict_proba(features)[0] best_classes = sorted(enumerate(prediction), key=lambda v: v[1], reverse=True) results = MultiLabelClassificationOutput() for class_index, probability in best_classes: results.labels.append( get_label( model=model, features=features, class_index=class_index, probability=probability, ) ) if sum(r.confidence for r in results.labels) >= target_confidence: break return results def get_label( model: Pipeline, features: np.ndarray, class_index: int, probability: float ) -> ClassificationOutput: return ClassificationOutput( label=model.named_steps[\"classifier\"].classes_[class_index], confidence=round(probability * 100), explanation=[ word for _, word in sorted( ( (weight, word) for weight, word, count in zip( model.named_steps[\"classifier\"].feature_log_prob_[class_index], model.named_steps[\"vectorizer\"].get_feature_names_out(), features.A[0], ) if count > 0 ), reverse=True, ) ][:5], ) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.4): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of small-domain-prediction Latest version of small-domain-prediction is 0 (from versions: 0) File small-domain-prediction-0 found in cache","title":"Create the inference function"},{"location":"examples/simple/deploy/#check-accuracy-on-the-test-split","text":"Anything under if __name__ == \"__main__\": will not be run when the script is executed by the great-ai CLI app. This, combined with query_ground_truth and the /traces/{trace_id}/feedback endpoint are ideal for creating a continuous-integration job for checking the quality of the model before deployment. In [2]: Copied! if __name__ == \"__main__\" : from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth ( \"test\" ) X = [ d . input for d in data ] y_actual = [ d . feedback for d in data ] y_predicted = [ d . output . labels [ 0 ] . label for d in predict_domain . process_batch ( X , do_not_persist_traces = True ) ] y_actual_aligned = [ p if p in a else a [ 0 ] for p , a in zip ( y_predicted , y_actual )] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_actual_aligned , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_actual_aligned , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) if __name__ == \"__main__\": from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth(\"test\") X = [d.input for d in data] y_actual = [d.feedback for d in data] y_predicted = [ d.output.labels[0].label for d in predict_domain.process_batch(X, do_not_persist_traces=True) ] y_actual_aligned = [p if p in a else a[0] for p, a in zip(y_predicted, y_actual)] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_actual_aligned, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_actual_aligned, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 12332/12332 [01:27<00:00, 141.08it/s] precision recall f1-score support Art 0.43 0.35 0.39 125 Biology 0.77 0.83 0.80 1209 Business 0.50 0.71 0.59 312 Chemistry 0.80 0.67 0.73 1194 Computer Science 0.77 0.76 0.77 1293 Economics 0.64 0.58 0.61 251 Engineering 0.55 0.53 0.54 815 Environmental Science 0.54 0.56 0.55 230 Geography 0.54 0.44 0.48 277 Geology 0.76 0.67 0.71 228 History 0.29 0.21 0.24 102 Materials Science 0.73 0.81 0.77 1053 Mathematics 0.80 0.70 0.75 551 Medicine 0.95 0.77 0.85 2794 Philosophy 0.61 0.12 0.21 88 Physics 0.67 0.76 0.72 605 Political Science 0.43 0.52 0.47 297 Psychology 0.53 0.80 0.63 600 Sociology 0.32 0.60 0.42 308 accuracy 0.71 12332 macro avg 0.61 0.60 0.59 12332 weighted avg 0.73 0.71 0.71 12332","title":"Check accuracy on the test split"},{"location":"examples/simple/stacked-bars/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); In [1]: Copied! from great_ai import query_ground_truth data = query_ground_truth ( \"train\" ) from great_ai import query_ground_truth data = query_ground_truth(\"train\") Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices In [9]: Copied! from collections import Counter import matplotlib.pyplot as plt from great_ai.utilities import unique import numpy as np import matplotlib.ticker as mtick first = Counter ( d . feedback [ 0 ] for d in data if len ( d . feedback ) > 0 ) second = Counter ( d . feedback [ 1 ] for d in data if len ( d . feedback ) > 1 ) third = Counter ( d . feedback [ 2 ] for d in data if len ( d . feedback ) > 2 ) domains = sorted ( unique ( domain for d in data for domain in d . feedback )) first = [ first [ d ] / len ( data ) for d in domains ] second = [ second [ d ] / len ( data ) for d in domains ] third = [ third [ d ] / len ( data ) for d in domains ] # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 20 plt . rcParams [ \"figure.figsize\" ] = ( 14 , 10 ) fig , ax = plt . subplots () plt . xticks ( rotation = 90 ) ax . bar ( domains , first , label = \"Primary domain\" ) ax . bar ( domains , second , label = \"Secondary domain\" , bottom = first ) ax . bar ( domains , third , label = \"Tertiary domain\" , bottom = np . add ( first , second )) ax . yaxis . set_major_formatter ( mtick . PercentFormatter ( 1 )) ax . legend () ax . set_ylabel ( \"Ratio of all publications\" ) fig . tight_layout () fig . savefig ( \"ss-distribution.png\" , dpi = 500 ) None from collections import Counter import matplotlib.pyplot as plt from great_ai.utilities import unique import numpy as np import matplotlib.ticker as mtick first = Counter(d.feedback[0] for d in data if len(d.feedback) > 0) second = Counter(d.feedback[1] for d in data if len(d.feedback) > 1) third = Counter(d.feedback[2] for d in data if len(d.feedback) > 2) domains = sorted(unique(domain for d in data for domain in d.feedback)) first = [first[d] / len(data) for d in domains] second = [second[d] / len(data) for d in domains] third = [third[d] / len(data) for d in domains] # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 20 plt.rcParams[\"figure.figsize\"] = (14, 10) fig, ax = plt.subplots() plt.xticks(rotation=90) ax.bar(domains, first, label=\"Primary domain\") ax.bar(domains, second, label=\"Secondary domain\", bottom=first) ax.bar(domains, third, label=\"Tertiary domain\", bottom=np.add(first, second)) ax.yaxis.set_major_formatter(mtick.PercentFormatter(1)) ax.legend() ax.set_ylabel(\"Ratio of all publications\") fig.tight_layout() fig.savefig(\"ss-distribution.png\", dpi=500) None","title":"Stacked bars"},{"location":"examples/simple/train/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); const range = document.createRange(); range.selectNodeContents(node); selection.addRange(range); document.execCommand('copy'); selection.removeAllRanges(); return Promise.resolve(); } function copyText(text) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(text); } const body = document.body; if (!body) { return Promise.reject(new Error()); } const node = createNode(text); body.appendChild(node); copyNode(node); body.removeChild(node); return Promise.resolve(); } function copy(button) { const id = button.getAttribute('for'); const text = button.getAttribute('value'); function trigger() { button.dispatchEvent(new CustomEvent('clipboard-copy', { bubbles: true })); } if (text) { copyText(text).then(trigger); } else if (id) { const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument; if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return; const node = root.getElementById(id); if (node) copyTarget(node).then(trigger); } } function copyTarget(content) { if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) { return copyText(content.value); } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) { return copyText(content.href); } else { return copyNode(content); } } function clicked(event) { const button = event.currentTarget; if (button instanceof HTMLElement) { copy(button); } } function keydown(event) { if (event.key === ' ' || event.key === 'Enter') { const button = event.currentTarget; if (button instanceof HTMLElement) { event.preventDefault(); copy(button); } } } function focused(event) { event.currentTarget.addEventListener('keydown', keydown); } function blurred(event) { event.currentTarget.removeEventListener('keydown', keydown); } class ClipboardCopyElement extends HTMLElement { constructor() { super(); this.addEventListener('click', clicked); this.addEventListener('focus', focused); this.addEventListener('blur', blurred); } connectedCallback() { if (!this.hasAttribute('tabindex')) { this.setAttribute('tabindex', '0'); } if (!this.hasAttribute('role')) { this.setAttribute('role', 'button'); } } get value() { return this.getAttribute('value') || ''; } set value(text) { this.setAttribute('value', text); } } if (!window.customElements.get('clipboard-copy')) { window.ClipboardCopyElement = ClipboardCopyElement; window.customElements.define('clipboard-copy', ClipboardCopyElement); } return ClipboardCopyElement; })); document.addEventListener('clipboard-copy', function(event) { const notice = event.target.querySelector('.notice') notice.hidden = false setTimeout(function() { notice.hidden = true }, 1000) }) pre { line-height: 125%; } td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Optimise and train a model \u00b6 The blue boxes show the steps implemented in this notebook. In the first part, we have cleaned and transformed our training data. We can now access this data using great_ai.LargeFile . Locally, it will gives us the cached version, otherwise, the latest version is downloaded from S3 or GridFS. In this part, we hyperparameter-optimise and train a simple Naive Bayes classifier which we then export for deployment using great_ai.save_model . Load data that has been extracted in part 1 \u00b6 In [1]: Copied! from great_ai import query_ground_truth data = query_ground_truth ( \"train\" ) X = [ d . input for d in data for domain in d . feedback ] y = [ domain for d in data for domain in d . feedback ] from great_ai import query_ground_truth data = query_ground_truth(\"train\") X = [d.input for d in data for domain in d.feedback] y = [domain for d in data for domain in d.feedback] Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices In [2]: Copied! from collections import Counter import matplotlib.pyplot as plt domains , counts = zip ( * Counter ( y ) . most_common ()) # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 20 , 5 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 plt . xticks ( rotation = 90 ) plt . bar ( domains , counts ) None from collections import Counter import matplotlib.pyplot as plt domains, counts = zip(*Counter(y).most_common()) # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.figsize\"] = (20, 5) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 plt.xticks(rotation=90) plt.bar(domains, counts) None Optimise and train Multinomial Naive Bayes classifier \u00b6 In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X , y ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X, y) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits --------------------------------------------------------------------------- NameError Traceback (most recent call last) /data/projects/great-ai/docs/examples/simple/train.ipynb Cell 6 in <cell line: 18> () <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=2'>3</a> optimisation_pipeline = GridSearchCV( <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=3'>4</a> create_pipeline(), <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=4'>5</a> { (...) <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=13'>14</a> verbose=1, <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=14'>15</a> ) <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=15'>16</a> optimisation_pipeline.fit(X, y) ---> <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=17'>18</a> results = pd.DataFrame(optimisation_pipeline.cv_results_) <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=18'>19</a> results.sort_values(\"rank_test_score\") NameError : name 'pd' is not defined In [ ]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X , y ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X, y) Out[ ]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=0.5, fit_prior=False))]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=0.5, fit_prior=False))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=0.5, fit_prior=False) Export the model using GreatAI \u00b6 In [ ]: Copied! from great_ai import save_model save_model ( classifier , key = \"small-domain-prediction\" , keep_last_n = 5 ) from great_ai import save_model save_model(classifier, key=\"small-domain-prediction\", keep_last_n=5) Fetching cached versions of small-domain-prediction Copying file for small-domain-prediction-1 Compressing small-domain-prediction-1 Model small-domain-prediction uploaded with version 1 Out[ ]: 'small-domain-prediction:1' Next: Part 3 \u00b6","title":"Optimise and train a model"},{"location":"examples/simple/train/#optimise-and-train-a-model","text":"The blue boxes show the steps implemented in this notebook. In the first part, we have cleaned and transformed our training data. We can now access this data using great_ai.LargeFile . Locally, it will gives us the cached version, otherwise, the latest version is downloaded from S3 or GridFS. In this part, we hyperparameter-optimise and train a simple Naive Bayes classifier which we then export for deployment using great_ai.save_model .","title":"Optimise and train a model"},{"location":"examples/simple/train/#load-data-that-has-been-extracted-in-part-1","text":"In [1]: Copied! from great_ai import query_ground_truth data = query_ground_truth ( \"train\" ) X = [ d . input for d in data for domain in d . feedback ] y = [ domain for d in data for domain in d . feedback ] from great_ai import query_ground_truth data = query_ground_truth(\"train\") X = [d.input for d in data for domain in d.feedback] y = [domain for d in data for domain in d.feedback] Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices In [2]: Copied! from collections import Counter import matplotlib.pyplot as plt domains , counts = zip ( * Counter ( y ) . most_common ()) # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 20 , 5 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 plt . xticks ( rotation = 90 ) plt . bar ( domains , counts ) None from collections import Counter import matplotlib.pyplot as plt domains, counts = zip(*Counter(y).most_common()) # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.figsize\"] = (20, 5) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 plt.xticks(rotation=90) plt.bar(domains, counts) None","title":"Load data that has been extracted in part 1"},{"location":"examples/simple/train/#optimise-and-train-multinomial-naive-bayes-classifier","text":"In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X , y ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X, y) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits --------------------------------------------------------------------------- NameError Traceback (most recent call last) /data/projects/great-ai/docs/examples/simple/train.ipynb Cell 6 in <cell line: 18> () <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=2'>3</a> optimisation_pipeline = GridSearchCV( <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=3'>4</a> create_pipeline(), <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=4'>5</a> { (...) <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=13'>14</a> verbose=1, <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=14'>15</a> ) <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=15'>16</a> optimisation_pipeline.fit(X, y) ---> <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=17'>18</a> results = pd.DataFrame(optimisation_pipeline.cv_results_) <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=18'>19</a> results.sort_values(\"rank_test_score\") NameError : name 'pd' is not defined In [ ]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X , y ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X, y) Out[ ]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=0.5, fit_prior=False))]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=0.5, fit_prior=False))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=0.5, fit_prior=False)","title":"Optimise and train Multinomial Naive Bayes classifier"},{"location":"examples/simple/train/#export-the-model-using-greatai","text":"In [ ]: Copied! from great_ai import save_model save_model ( classifier , key = \"small-domain-prediction\" , keep_last_n = 5 ) from great_ai import save_model save_model(classifier, key=\"small-domain-prediction\", keep_last_n=5) Fetching cached versions of small-domain-prediction Copying file for small-domain-prediction-1 Compressing small-domain-prediction-1 Model small-domain-prediction uploaded with version 1 Out[ ]: 'small-domain-prediction:1'","title":"Export the model using GreatAI"},{"location":"examples/simple/train/#next-part-3","text":"","title":"Next: Part 3"},{"location":"examples/simple-mag/train/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); const range = document.createRange(); range.selectNodeContents(node); selection.addRange(range); document.execCommand('copy'); selection.removeAllRanges(); return Promise.resolve(); } function copyText(text) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(text); } const body = document.body; if (!body) { return Promise.reject(new Error()); } const node = createNode(text); body.appendChild(node); copyNode(node); body.removeChild(node); return Promise.resolve(); } function copy(button) { const id = button.getAttribute('for'); const text = button.getAttribute('value'); function trigger() { button.dispatchEvent(new CustomEvent('clipboard-copy', { bubbles: true })); } if (text) { copyText(text).then(trigger); } else if (id) { const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument; if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return; const node = root.getElementById(id); if (node) copyTarget(node).then(trigger); } } function copyTarget(content) { if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) { return copyText(content.value); } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) { return copyText(content.href); } else { return copyNode(content); } } function clicked(event) { const button = event.currentTarget; if (button instanceof HTMLElement) { copy(button); } } function keydown(event) { if (event.key === ' ' || event.key === 'Enter') { const button = event.currentTarget; if (button instanceof HTMLElement) { event.preventDefault(); copy(button); } } } function focused(event) { event.currentTarget.addEventListener('keydown', keydown); } function blurred(event) { event.currentTarget.removeEventListener('keydown', keydown); } class ClipboardCopyElement extends HTMLElement { constructor() { super(); this.addEventListener('click', clicked); this.addEventListener('focus', focused); this.addEventListener('blur', blurred); } connectedCallback() { if (!this.hasAttribute('tabindex')) { this.setAttribute('tabindex', '0'); } if (!this.hasAttribute('role')) { this.setAttribute('role', 'button'); } } get value() { return this.getAttribute('value') || ''; } set value(text) { this.setAttribute('value', text); } } if (!window.customElements.get('clipboard-copy')) { window.ClipboardCopyElement = ClipboardCopyElement; window.customElements.define('clipboard-copy', ClipboardCopyElement); } return ClipboardCopyElement; })); document.addEventListener('clipboard-copy', function(event) { const notice = event.target.querySelector('.notice') notice.hidden = false setTimeout(function() { notice.hidden = true }, 1000) }) pre { line-height: 125%; } td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Train a field of study (domain) classification of sentences on the MAG dataset \u00b6 SciBERT achieves an F1-score of 0.6571 on this dataset. This notebook shows that better results can be achieved without even using transformers. In [1]: Copied! import json from typing import Tuple from great_ai.utilities import clean , simple_parallel_map from tqdm.cli import tqdm def preprocess ( line : str ) -> Tuple [ str , str ]: data_point = json . loads ( line ) return ( clean ( data_point [ \"text\" ], convert_to_ascii = True ), data_point [ \"label\" ]) with open ( \"../../tutorial/data/train.txt\" , encoding = \"utf-8\" ) as f : training_data = simple_parallel_map ( preprocess , f . readlines ()) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] with open ( \"../../tutorial/data/test.txt\" , encoding = \"utf-8\" ) as f : test_data = simple_parallel_map ( preprocess , f . readlines ()) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] import json from typing import Tuple from great_ai.utilities import clean, simple_parallel_map from tqdm.cli import tqdm def preprocess(line: str) -> Tuple[str, str]: data_point = json.loads(line) return (clean(data_point[\"text\"], convert_to_ascii=True), data_point[\"label\"]) with open(\"../../tutorial/data/train.txt\", encoding=\"utf-8\") as f: training_data = simple_parallel_map(preprocess, f.readlines()) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] with open(\"../../tutorial/data/test.txt\", encoding=\"utf-8\") as f: test_data = simple_parallel_map(preprocess, f.readlines()) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84000/84000 [00:08<00:00, 9514.74it/s] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22399/22399 [00:02<00:00, 7728.92it/s] In [2]: Copied! from collections import Counter import matplotlib.pyplot as plt domains , counts = zip ( * Counter ( y_train ) . most_common ()) # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 18 plt . rcParams [ \"figure.figsize\" ] = ( 8 , 5 ) fig , ax = plt . subplots () plt . xticks ( rotation = 90 ) ax . bar ( domains , counts ) ax . set_ylabel ( \"Count\" ) fig . tight_layout () fig . savefig ( \"mag-distribution.png\" , dpi = 500 ) None from collections import Counter import matplotlib.pyplot as plt domains, counts = zip(*Counter(y_train).most_common()) # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 18 plt.rcParams[\"figure.figsize\"] = (8, 5) fig, ax = plt.subplots() plt.xticks(rotation=90) ax.bar(domains, counts) ax.set_ylabel(\"Count\") fig.tight_layout() fig.savefig(\"mag-distribution.png\", dpi=500) None In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X_train , y_train ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X_train, y_train) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 12 1.410234 0.165616 0.616949 0.017791 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.677283 0.670398 0.665671 0.671118 0.004768 1 15 1.356129 0.033539 0.799676 0.077166 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.676970 0.670244 0.666123 0.671112 0.004470 2 18 1.191132 0.046271 0.633976 0.009344 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675678 0.669321 0.665305 0.670101 0.004271 3 21 1.206345 0.121633 0.494508 0.101456 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675569 0.669482 0.665225 0.670092 0.004245 4 3 1.434749 0.077467 0.665522 0.042225 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675225 0.668912 0.665652 0.669930 0.003974 5 0 1.338873 0.100248 0.791040 0.061094 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675264 0.668781 0.665394 0.669813 0.004095 6 9 1.429841 0.071771 0.868423 0.097744 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.674199 0.668005 0.664456 0.668887 0.004026 7 6 1.320994 0.115772 0.708544 0.028758 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.673996 0.668048 0.664572 0.668872 0.003891 8 13 1.430184 0.068179 0.667534 0.045122 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656459 0.647433 0.644864 0.649585 0.004972 9 16 1.230683 0.097298 0.657511 0.015534 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656196 0.646960 0.645436 0.649530 0.004754 10 4 1.247198 0.061587 0.684918 0.085735 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654424 0.644690 0.644991 0.648035 0.004519 11 1 1.367997 0.161019 0.720563 0.047895 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654622 0.644901 0.644425 0.647983 0.004699 12 19 1.276051 0.053059 0.670412 0.055486 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.655125 0.645586 0.643166 0.647959 0.005163 13 22 1.098513 0.062989 0.454028 0.049762 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.654254 0.645409 0.643514 0.647726 0.004680 14 7 1.246671 0.070173 0.678239 0.061924 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652727 0.643691 0.642749 0.646389 0.004498 15 10 1.335585 0.055454 0.714089 0.031715 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652290 0.642975 0.643421 0.646229 0.004290 16 14 1.433829 0.102381 0.584196 0.020291 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.587097 0.577411 0.580170 0.581560 0.004075 17 17 1.259510 0.128875 0.561018 0.006103 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.585713 0.577197 0.580763 0.581224 0.003492 18 2 1.369987 0.103349 0.699614 0.033642 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.586346 0.576987 0.579852 0.581062 0.003915 19 5 1.292009 0.083116 0.621565 0.058068 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.585634 0.576217 0.580320 0.580723 0.003855 20 20 1.158813 0.039281 0.524616 0.016054 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.581096 0.572833 0.576280 0.576736 0.003389 21 8 1.302560 0.095545 0.709228 0.144463 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.580841 0.572391 0.575731 0.576321 0.003475 22 23 1.093018 0.033991 0.331076 0.005935 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.579769 0.572373 0.576699 0.576280 0.003034 23 11 1.534622 0.081777 0.649834 0.033149 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.579743 0.571607 0.576352 0.575901 0.003337 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X_train , y_train ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X_train, y_train) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=1) In [6]: Copied! from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier . predict ( X_test ) % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_test , y_predicted , digits = 4 )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_test , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) None from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier.predict(X_test) %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_test, y_predicted, digits=4)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_test, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) None precision recall f1-score support business 0.6412 0.7258 0.6808 3198 economics 0.6635 0.7146 0.6881 3189 geography 0.7461 0.6791 0.7111 3207 medicine 0.8806 0.9021 0.8912 3187 politics 0.5563 0.5895 0.5724 3169 psychology 0.7654 0.6811 0.7208 3252 sociology 0.5165 0.4698 0.4921 3197 accuracy 0.6803 22399 macro avg 0.6814 0.6803 0.6795 22399 weighted avg 0.6817 0.6803 0.6796 22399","title":"Train a field of study (domain) classification of sentences on the MAG dataset"},{"location":"examples/simple-mag/train/#train-a-field-of-study-domain-classification-of-sentences-on-the-mag-dataset","text":"SciBERT achieves an F1-score of 0.6571 on this dataset. This notebook shows that better results can be achieved without even using transformers. In [1]: Copied! import json from typing import Tuple from great_ai.utilities import clean , simple_parallel_map from tqdm.cli import tqdm def preprocess ( line : str ) -> Tuple [ str , str ]: data_point = json . loads ( line ) return ( clean ( data_point [ \"text\" ], convert_to_ascii = True ), data_point [ \"label\" ]) with open ( \"../../tutorial/data/train.txt\" , encoding = \"utf-8\" ) as f : training_data = simple_parallel_map ( preprocess , f . readlines ()) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] with open ( \"../../tutorial/data/test.txt\" , encoding = \"utf-8\" ) as f : test_data = simple_parallel_map ( preprocess , f . readlines ()) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] import json from typing import Tuple from great_ai.utilities import clean, simple_parallel_map from tqdm.cli import tqdm def preprocess(line: str) -> Tuple[str, str]: data_point = json.loads(line) return (clean(data_point[\"text\"], convert_to_ascii=True), data_point[\"label\"]) with open(\"../../tutorial/data/train.txt\", encoding=\"utf-8\") as f: training_data = simple_parallel_map(preprocess, f.readlines()) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] with open(\"../../tutorial/data/test.txt\", encoding=\"utf-8\") as f: test_data = simple_parallel_map(preprocess, f.readlines()) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84000/84000 [00:08<00:00, 9514.74it/s] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22399/22399 [00:02<00:00, 7728.92it/s] In [2]: Copied! from collections import Counter import matplotlib.pyplot as plt domains , counts = zip ( * Counter ( y_train ) . most_common ()) # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 18 plt . rcParams [ \"figure.figsize\" ] = ( 8 , 5 ) fig , ax = plt . subplots () plt . xticks ( rotation = 90 ) ax . bar ( domains , counts ) ax . set_ylabel ( \"Count\" ) fig . tight_layout () fig . savefig ( \"mag-distribution.png\" , dpi = 500 ) None from collections import Counter import matplotlib.pyplot as plt domains, counts = zip(*Counter(y_train).most_common()) # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 18 plt.rcParams[\"figure.figsize\"] = (8, 5) fig, ax = plt.subplots() plt.xticks(rotation=90) ax.bar(domains, counts) ax.set_ylabel(\"Count\") fig.tight_layout() fig.savefig(\"mag-distribution.png\", dpi=500) None In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X_train , y_train ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X_train, y_train) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 12 1.410234 0.165616 0.616949 0.017791 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.677283 0.670398 0.665671 0.671118 0.004768 1 15 1.356129 0.033539 0.799676 0.077166 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.676970 0.670244 0.666123 0.671112 0.004470 2 18 1.191132 0.046271 0.633976 0.009344 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675678 0.669321 0.665305 0.670101 0.004271 3 21 1.206345 0.121633 0.494508 0.101456 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675569 0.669482 0.665225 0.670092 0.004245 4 3 1.434749 0.077467 0.665522 0.042225 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675225 0.668912 0.665652 0.669930 0.003974 5 0 1.338873 0.100248 0.791040 0.061094 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675264 0.668781 0.665394 0.669813 0.004095 6 9 1.429841 0.071771 0.868423 0.097744 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.674199 0.668005 0.664456 0.668887 0.004026 7 6 1.320994 0.115772 0.708544 0.028758 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.673996 0.668048 0.664572 0.668872 0.003891 8 13 1.430184 0.068179 0.667534 0.045122 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656459 0.647433 0.644864 0.649585 0.004972 9 16 1.230683 0.097298 0.657511 0.015534 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656196 0.646960 0.645436 0.649530 0.004754 10 4 1.247198 0.061587 0.684918 0.085735 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654424 0.644690 0.644991 0.648035 0.004519 11 1 1.367997 0.161019 0.720563 0.047895 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654622 0.644901 0.644425 0.647983 0.004699 12 19 1.276051 0.053059 0.670412 0.055486 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.655125 0.645586 0.643166 0.647959 0.005163 13 22 1.098513 0.062989 0.454028 0.049762 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.654254 0.645409 0.643514 0.647726 0.004680 14 7 1.246671 0.070173 0.678239 0.061924 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652727 0.643691 0.642749 0.646389 0.004498 15 10 1.335585 0.055454 0.714089 0.031715 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652290 0.642975 0.643421 0.646229 0.004290 16 14 1.433829 0.102381 0.584196 0.020291 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.587097 0.577411 0.580170 0.581560 0.004075 17 17 1.259510 0.128875 0.561018 0.006103 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.585713 0.577197 0.580763 0.581224 0.003492 18 2 1.369987 0.103349 0.699614 0.033642 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.586346 0.576987 0.579852 0.581062 0.003915 19 5 1.292009 0.083116 0.621565 0.058068 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.585634 0.576217 0.580320 0.580723 0.003855 20 20 1.158813 0.039281 0.524616 0.016054 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.581096 0.572833 0.576280 0.576736 0.003389 21 8 1.302560 0.095545 0.709228 0.144463 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.580841 0.572391 0.575731 0.576321 0.003475 22 23 1.093018 0.033991 0.331076 0.005935 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.579769 0.572373 0.576699 0.576280 0.003034 23 11 1.534622 0.081777 0.649834 0.033149 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.579743 0.571607 0.576352 0.575901 0.003337 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X_train , y_train ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X_train, y_train) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=1) In [6]: Copied! from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier . predict ( X_test ) % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_test , y_predicted , digits = 4 )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_test , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) None from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier.predict(X_test) %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_test, y_predicted, digits=4)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_test, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) None precision recall f1-score support business 0.6412 0.7258 0.6808 3198 economics 0.6635 0.7146 0.6881 3189 geography 0.7461 0.6791 0.7111 3207 medicine 0.8806 0.9021 0.8912 3187 politics 0.5563 0.5895 0.5724 3169 psychology 0.7654 0.6811 0.7208 3252 sociology 0.5165 0.4698 0.4921 3197 accuracy 0.6803 22399 macro avg 0.6814 0.6803 0.6795 22399 weighted avg 0.6817 0.6803 0.6796 22399","title":"Train a field of study (domain) classification of sentences on the MAG dataset"},{"location":"how-to-guides/call-remote/","text":"How to call remote GreatAI instances # Microservices architecture (or SOA ) work well with ML applications. This is because their interfaces are usually very narrow, while the functionality provided quite comprehensive. Hence, drawing the boundaries of responsibilities is more straightforward in the case of ML services than in the case of more traditional business applications. For this reason, it is common to have a tree of models (preferably wrapped in GreatAI instances) communicating with each other. Although regular HTTP POST requests could be sent to each service's /predict endpoint, great-ai comes with two convenience functions: call_remote_great_ai and call_remote_great_ai_async to wrap this request. These provide you with some level of robustness and deserialisation. Inside notebooks The async variant, call_remote_great_ai_async , requires a running event loop while the synchronous variant disallows other running event-loops. Therefore, when running inside a Jupyter Notebook, always call call_remote_great_ai_async . Simple example # Let's create two processes: a server and a client. Server # server.py from great_ai import GreatAI from asyncio import sleep @GreatAI . create async def slow_greeter ( your_name ): await sleep ( 2 ) return f 'Hi { your_name } !' Run this in development mode by executing great-ai server.py or python3 -m great_ai server.py if you're on Windows and great-ai is not in your PATH . Client # client.py from great_ai import call_remote_great_ai names = [ 'Oliv\u00e9r' , 'Bal\u00e1zs' , 'Andr\u00e1s' ] results = [ call_remote_great_ai ( 'http://localhost:6060' , { 'your_name' : name } ) . output #(1) for name in names ] print ( results ) Only return the outputs so we don't clutter up the terminal. Run this script as a regular Python script by executing python3 client.py . As you can see, everything worked as expected. There is one way to improve it though. An async example # Let's send multiple requests at the same time to speed up the overall execution time. To do this, we will use the call_remote_great_ai_async function. Why is this possible? Note, that in server.py , the inference function is declared async . This means that multiple \"copies\" of it can run at the same time in the same thread. Since, there is no CPU bottleneck, the server has a quite large throughput (requests responded to per second), but its latency will stay around 2 seconds due to the async sleep command. If your great-ai server is not async , higher throughput can be achieved by running multiple instances of it, either manually, or by running it with multiple uvicorn workers like this: ENVIRONMENT=production great-ai server.py --worker_count 4 Async client # async-client.py from great_ai import call_remote_great_ai_async import asyncio names = [ 'Oliv\u00e9r' , 'Bal\u00e1zs' , 'Andr\u00e1s' ] async def main (): futures = [ call_remote_great_ai_async ( 'http://localhost:6060' , { 'your_name' : name } ) for name in names ] results = await asyncio . gather ( * futures ) print ([ r . output for r in results ]) asyncio . run ( main ()) Replace client.py with this async client. Note that even though async support is significantly more streamlined in recent Python versions, it still requires a bit more boilerplate than its synchronous counterpart. This also works, and in some use-cases might be considerably quicker.","title":"How to call remote GreatAI instances"},{"location":"how-to-guides/call-remote/#how-to-call-remote-greatai-instances","text":"Microservices architecture (or SOA ) work well with ML applications. This is because their interfaces are usually very narrow, while the functionality provided quite comprehensive. Hence, drawing the boundaries of responsibilities is more straightforward in the case of ML services than in the case of more traditional business applications. For this reason, it is common to have a tree of models (preferably wrapped in GreatAI instances) communicating with each other. Although regular HTTP POST requests could be sent to each service's /predict endpoint, great-ai comes with two convenience functions: call_remote_great_ai and call_remote_great_ai_async to wrap this request. These provide you with some level of robustness and deserialisation. Inside notebooks The async variant, call_remote_great_ai_async , requires a running event loop while the synchronous variant disallows other running event-loops. Therefore, when running inside a Jupyter Notebook, always call call_remote_great_ai_async .","title":"How to call remote GreatAI instances"},{"location":"how-to-guides/call-remote/#simple-example","text":"Let's create two processes: a server and a client.","title":"Simple example"},{"location":"how-to-guides/call-remote/#server","text":"server.py from great_ai import GreatAI from asyncio import sleep @GreatAI . create async def slow_greeter ( your_name ): await sleep ( 2 ) return f 'Hi { your_name } !' Run this in development mode by executing great-ai server.py or python3 -m great_ai server.py if you're on Windows and great-ai is not in your PATH .","title":"Server"},{"location":"how-to-guides/call-remote/#client","text":"client.py from great_ai import call_remote_great_ai names = [ 'Oliv\u00e9r' , 'Bal\u00e1zs' , 'Andr\u00e1s' ] results = [ call_remote_great_ai ( 'http://localhost:6060' , { 'your_name' : name } ) . output #(1) for name in names ] print ( results ) Only return the outputs so we don't clutter up the terminal. Run this script as a regular Python script by executing python3 client.py . As you can see, everything worked as expected. There is one way to improve it though.","title":"Client"},{"location":"how-to-guides/call-remote/#an-async-example","text":"Let's send multiple requests at the same time to speed up the overall execution time. To do this, we will use the call_remote_great_ai_async function. Why is this possible? Note, that in server.py , the inference function is declared async . This means that multiple \"copies\" of it can run at the same time in the same thread. Since, there is no CPU bottleneck, the server has a quite large throughput (requests responded to per second), but its latency will stay around 2 seconds due to the async sleep command. If your great-ai server is not async , higher throughput can be achieved by running multiple instances of it, either manually, or by running it with multiple uvicorn workers like this: ENVIRONMENT=production great-ai server.py --worker_count 4","title":"An async example"},{"location":"how-to-guides/call-remote/#async-client","text":"async-client.py from great_ai import call_remote_great_ai_async import asyncio names = [ 'Oliv\u00e9r' , 'Bal\u00e1zs' , 'Andr\u00e1s' ] async def main (): futures = [ call_remote_great_ai_async ( 'http://localhost:6060' , { 'your_name' : name } ) for name in names ] results = await asyncio . gather ( * futures ) print ([ r . output for r in results ]) asyncio . run ( main ()) Replace client.py with this async client. Note that even though async support is significantly more streamlined in recent Python versions, it still requires a bit more boilerplate than its synchronous counterpart. This also works, and in some use-cases might be considerably quicker.","title":"Async client"},{"location":"how-to-guides/configure-service/","text":"How to configure GreatAI # GreatAI aims to provide reasonable defaults wherever possible. The current configuration is always prominently displayed (and updated) on the dashboard and in the command-line start-up banner. Using great_ai.configure # You can override any of the default settings by calling great_ai.configure . If you don't call configure , the default settings are applied on the first call to most great-ai functions. Warning You must call great_ai.configure before calling (or decorating with) any other great-ai function. However, importing other functions before calling great_ai.configure is permitted. configure-demo.py from great_ai import configure , RouteConfig import logging configure ( version = '1.0.0' , log_level = logging . INFO , seed = 2 , should_log_exception_stack = False , prediction_cache_size = 0 , #(1) disable_se4ml_banner = True , dashboard_table_size = 200 , route_config = RouteConfig ( #(2) feedback_endpoints_enabled = False , dashboard_enabled = False ) ) Completely disable caching. The unspecified routes are enabled by default. Using remote storage # The only aspect that cannot be automated is choosing the backing storage for the database and file storage. Right now, you have 3 options for storing the models and large datasets: LargeFileLocal , LargeFileMongo , and LargeFileS3 . Without explicit configuration, LargeFileLocal is selected by default. This one still version-controls your files but it only stores them in a local path (which of course can be a remote volume attached by NFS , HDFS , etc.). Important If your working directory contains a mongo.ini or s3.ini file, an attempt is made to auto-configure LargeFileMongo or LargeFileS3 respectively. To use LargeFileMongo or LargeFileS3 explicitly, configure them before calling any other great-ai function. S3-compatible # s3.ini aws_region_name = eu-west-2 aws_access_key_id = MY_AWS_ACCESS_KEY # ENV:MY_AWS_ACCESS_KEY would also work aws_secret_access_key = MY_AWS_SECRET_KEY large_files_bucket_name = bucket-for-models use-s3.py from great_ai.large_file import LargeFileS3 from great_ai import save_model LargeFileS3 . configure_credentials_from_file ( 's3.ini' ) #(1) model = [ 4 , 3 ] save_model ( model , 'my-model' ) This line isn't strictly necessary because if s3.ini (or mongo.ini ) is available in the current working directory, they are automatically used to configure their respective LargeFile implementations/databases. Departing from AWS With the aws_endpoint_url argument, it is possible to use any other S3-compatible service such as Backblaze . In that case, it would be aws_endpoint_url=https://s3.us-west-002.backblazeb2.com . GridFS # GridFS specifies how to store files in MongoDB. The official MongoDB server and many compatible implementations support it. mongo.ini MONGO_CONNECTION_STRING = mongodb : // localhost : 27017 # this is the default value # if `MONGO_CONNECTION_STRING` is specified, this default is overridden MONGO_CONNECTION_STRING = ENV : MONGO_CONNECTION_STRING MONGO_DATABASE = my-database # it is automatically created if doesn't exist use-mongo.py from great_ai.large_file import LargeFileMongo from great_ai import save_model LargeFileMongo . configure_credentials_from_file ( 'mongo.ini' ) model = [ 4 , 3 ] save_model ( model , 'my-model' ) Simplifying config files You can combine mongo.ini or s3.ini with your application's config file because the unneeded keys are ignored by the configure_credentials_from_file method. Using a database # By default, a thread-safe version of TinyDB is utilised for saving the prediction traces into a local file. Unfortunately, for most production needs, this method is not suitable. MongoDB # At the moment, only MongoDB is supported as a production-ready TracingDatabase . In order to use it, you have to either place a file named mongo.ini in your working directory, or explicitly call either MongoDbDriver.configure_credentials_from_file or MongoDbDriver.configure_credentials .","title":"How to configure GreatAI"},{"location":"how-to-guides/configure-service/#how-to-configure-greatai","text":"GreatAI aims to provide reasonable defaults wherever possible. The current configuration is always prominently displayed (and updated) on the dashboard and in the command-line start-up banner.","title":"How to configure GreatAI"},{"location":"how-to-guides/configure-service/#using-great_aiconfigure","text":"You can override any of the default settings by calling great_ai.configure . If you don't call configure , the default settings are applied on the first call to most great-ai functions. Warning You must call great_ai.configure before calling (or decorating with) any other great-ai function. However, importing other functions before calling great_ai.configure is permitted. configure-demo.py from great_ai import configure , RouteConfig import logging configure ( version = '1.0.0' , log_level = logging . INFO , seed = 2 , should_log_exception_stack = False , prediction_cache_size = 0 , #(1) disable_se4ml_banner = True , dashboard_table_size = 200 , route_config = RouteConfig ( #(2) feedback_endpoints_enabled = False , dashboard_enabled = False ) ) Completely disable caching. The unspecified routes are enabled by default.","title":"Using great_ai.configure"},{"location":"how-to-guides/configure-service/#using-remote-storage","text":"The only aspect that cannot be automated is choosing the backing storage for the database and file storage. Right now, you have 3 options for storing the models and large datasets: LargeFileLocal , LargeFileMongo , and LargeFileS3 . Without explicit configuration, LargeFileLocal is selected by default. This one still version-controls your files but it only stores them in a local path (which of course can be a remote volume attached by NFS , HDFS , etc.). Important If your working directory contains a mongo.ini or s3.ini file, an attempt is made to auto-configure LargeFileMongo or LargeFileS3 respectively. To use LargeFileMongo or LargeFileS3 explicitly, configure them before calling any other great-ai function.","title":"Using remote storage"},{"location":"how-to-guides/configure-service/#s3-compatible","text":"s3.ini aws_region_name = eu-west-2 aws_access_key_id = MY_AWS_ACCESS_KEY # ENV:MY_AWS_ACCESS_KEY would also work aws_secret_access_key = MY_AWS_SECRET_KEY large_files_bucket_name = bucket-for-models use-s3.py from great_ai.large_file import LargeFileS3 from great_ai import save_model LargeFileS3 . configure_credentials_from_file ( 's3.ini' ) #(1) model = [ 4 , 3 ] save_model ( model , 'my-model' ) This line isn't strictly necessary because if s3.ini (or mongo.ini ) is available in the current working directory, they are automatically used to configure their respective LargeFile implementations/databases. Departing from AWS With the aws_endpoint_url argument, it is possible to use any other S3-compatible service such as Backblaze . In that case, it would be aws_endpoint_url=https://s3.us-west-002.backblazeb2.com .","title":"S3-compatible"},{"location":"how-to-guides/configure-service/#gridfs","text":"GridFS specifies how to store files in MongoDB. The official MongoDB server and many compatible implementations support it. mongo.ini MONGO_CONNECTION_STRING = mongodb : // localhost : 27017 # this is the default value # if `MONGO_CONNECTION_STRING` is specified, this default is overridden MONGO_CONNECTION_STRING = ENV : MONGO_CONNECTION_STRING MONGO_DATABASE = my-database # it is automatically created if doesn't exist use-mongo.py from great_ai.large_file import LargeFileMongo from great_ai import save_model LargeFileMongo . configure_credentials_from_file ( 'mongo.ini' ) model = [ 4 , 3 ] save_model ( model , 'my-model' ) Simplifying config files You can combine mongo.ini or s3.ini with your application's config file because the unneeded keys are ignored by the configure_credentials_from_file method.","title":"GridFS"},{"location":"how-to-guides/configure-service/#using-a-database","text":"By default, a thread-safe version of TinyDB is utilised for saving the prediction traces into a local file. Unfortunately, for most production needs, this method is not suitable.","title":"Using a database"},{"location":"how-to-guides/configure-service/#mongodb","text":"At the moment, only MongoDB is supported as a production-ready TracingDatabase . In order to use it, you have to either place a file named mongo.ini in your working directory, or explicitly call either MongoDbDriver.configure_credentials_from_file or MongoDbDriver.configure_credentials .","title":"MongoDB"},{"location":"how-to-guides/create-service/","text":"How to create a GreatAI service # The core value of great-ai lies in its GreatAI class. In order to take advantage of it, you need to create an instance wrapping your code. Let's say that you have the following greeter function: greeter.py def my_greeter_function ( your_name ): return f 'Hi { your_name } !' You can simply decorate (wrap) this function using the @GreatAI.create factory. greeter.py from great_ai import GreatAI @GreatAI . create def greeter ( your_name ): return f 'Hi { your_name } !' Why not simply use @GreatAI? The purpose of @GreatAI.create is simply to provide you with type-checking through MyPy, Pylance, and similar libraries. However, the overloading support for __new__ is lacking in MyPy, thus, a static factory method is used instead. With types # Even though it's not required by GreatAI, type annotating your codebase can save you from lots of trivial mistakes, that's why it's highly advised. Simply add the expected types to your function's signature. type_safe_greeter.py from great_ai import GreatAI @GreatAI . create def type_safe_greeter ( your_name : str ) -> str : return f 'Hi { your_name } !' This not only allows you to statically typecheck your code, but by default, GreatAI will check it during runtime as well using typeguard . With async # Asynchronous code can result in immense performance gains in certain cases. For example, you might rely on a third-party service, do database access, or call a remote GreatAI instance . In these cases, you can simply make your function async without any other changes. async_greeter.py from great_ai import GreatAI from asyncio import sleep @GreatAI . create async def async_greeter ( your_name : str ) -> str : await sleep ( 2 ) # simulate IO-bound operation return f 'Hi { your_name } !' With decorators # GreatAI can decorate already decorated functions. The only restriction is that @GreatAI.create must come last. There are two built-in decorators that you can use to customise your function but you can you use any third-party decorator as well. Using @use_model # If you have previously saved a model with save_model , you can inject it into your function by calling @use_model . greeter_with_model.py from great_ai import GreatAI , use_model @GreatAI . create @use_model ( 'name_of_my_model' , version = 'latest' ) #(1) def type_safe_greeter ( your_name : str , model ) -> str : return f 'Hi { your_name } !' assert type_safe_greeter ( 'Andras' ) . output == 'Hi Andras' By default, the parameter named model will be replaced by the loaded model. This behaviour can be customised by setting the model_kwarg_name . This way, even multiple models can be injected into a single function. Important You must call @use_model before @GreatAI.create . Note, that decorators are applied starting from the bottom-most one. Feel free to use @use_model in other places of the codebase, it works equally well outside of GreatAI services. Using @parameter # If you wish to turn off logging or specify custom validation for your parameters, you can use the @parameter decorator. Note By default, all parameters that are not affected by an explicit @parameter or @use_model are automatically decorated with @parameter when @GreatAI.create is called. greeter_with_validation.py from great_ai import GreatAI , use_model @GreatAI . create @parameter ( 'your_name' , disable_logging = True ) def type_safe_greeter ( your_name : str , model ) -> str : return f 'Hi { your_name } !' assert type_safe_greeter ( 'Andras' ) . output == 'Hi Andras' Important You must call @parameter before @GreatAI.create . Note, that decorators are applied starting from the bottom-most one. Feel free to use @parameter in other places of the codebase, it works equally well outside of GreatAI services. Complex example # Refer to the following example summarising the options you have when instantiating a GreatAI service. complex.py from great_ai import save_model , GreatAI , parameter , use_model , log_metric save_model ( 4 , 'secret-number' ) #(1) @GreatAI . create @parameter ( 'positive_number' , validate = lambda n : n > 0 , disable_logging = True ) @use_model ( 'secret-number' , version = 'latest' , model_kwarg_name = 'secret' ) def add_number ( positive_number : int , secret : int ) -> int : log_metric ( 'log directly into the returned Trace' , positive_number * 2 ) return positive_number + secret assert add_number ( 1 ) . output == 5 Refer to the configuration page for specifying where to store your models.","title":"How to create a GreatAI service"},{"location":"how-to-guides/create-service/#how-to-create-a-greatai-service","text":"The core value of great-ai lies in its GreatAI class. In order to take advantage of it, you need to create an instance wrapping your code. Let's say that you have the following greeter function: greeter.py def my_greeter_function ( your_name ): return f 'Hi { your_name } !' You can simply decorate (wrap) this function using the @GreatAI.create factory. greeter.py from great_ai import GreatAI @GreatAI . create def greeter ( your_name ): return f 'Hi { your_name } !' Why not simply use @GreatAI? The purpose of @GreatAI.create is simply to provide you with type-checking through MyPy, Pylance, and similar libraries. However, the overloading support for __new__ is lacking in MyPy, thus, a static factory method is used instead.","title":"How to create a GreatAI service"},{"location":"how-to-guides/create-service/#with-types","text":"Even though it's not required by GreatAI, type annotating your codebase can save you from lots of trivial mistakes, that's why it's highly advised. Simply add the expected types to your function's signature. type_safe_greeter.py from great_ai import GreatAI @GreatAI . create def type_safe_greeter ( your_name : str ) -> str : return f 'Hi { your_name } !' This not only allows you to statically typecheck your code, but by default, GreatAI will check it during runtime as well using typeguard .","title":"With types"},{"location":"how-to-guides/create-service/#with-async","text":"Asynchronous code can result in immense performance gains in certain cases. For example, you might rely on a third-party service, do database access, or call a remote GreatAI instance . In these cases, you can simply make your function async without any other changes. async_greeter.py from great_ai import GreatAI from asyncio import sleep @GreatAI . create async def async_greeter ( your_name : str ) -> str : await sleep ( 2 ) # simulate IO-bound operation return f 'Hi { your_name } !'","title":"With async"},{"location":"how-to-guides/create-service/#with-decorators","text":"GreatAI can decorate already decorated functions. The only restriction is that @GreatAI.create must come last. There are two built-in decorators that you can use to customise your function but you can you use any third-party decorator as well.","title":"With decorators"},{"location":"how-to-guides/create-service/#using-use_model","text":"If you have previously saved a model with save_model , you can inject it into your function by calling @use_model . greeter_with_model.py from great_ai import GreatAI , use_model @GreatAI . create @use_model ( 'name_of_my_model' , version = 'latest' ) #(1) def type_safe_greeter ( your_name : str , model ) -> str : return f 'Hi { your_name } !' assert type_safe_greeter ( 'Andras' ) . output == 'Hi Andras' By default, the parameter named model will be replaced by the loaded model. This behaviour can be customised by setting the model_kwarg_name . This way, even multiple models can be injected into a single function. Important You must call @use_model before @GreatAI.create . Note, that decorators are applied starting from the bottom-most one. Feel free to use @use_model in other places of the codebase, it works equally well outside of GreatAI services.","title":"Using @use_model"},{"location":"how-to-guides/create-service/#using-parameter","text":"If you wish to turn off logging or specify custom validation for your parameters, you can use the @parameter decorator. Note By default, all parameters that are not affected by an explicit @parameter or @use_model are automatically decorated with @parameter when @GreatAI.create is called. greeter_with_validation.py from great_ai import GreatAI , use_model @GreatAI . create @parameter ( 'your_name' , disable_logging = True ) def type_safe_greeter ( your_name : str , model ) -> str : return f 'Hi { your_name } !' assert type_safe_greeter ( 'Andras' ) . output == 'Hi Andras' Important You must call @parameter before @GreatAI.create . Note, that decorators are applied starting from the bottom-most one. Feel free to use @parameter in other places of the codebase, it works equally well outside of GreatAI services.","title":"Using @parameter"},{"location":"how-to-guides/create-service/#complex-example","text":"Refer to the following example summarising the options you have when instantiating a GreatAI service. complex.py from great_ai import save_model , GreatAI , parameter , use_model , log_metric save_model ( 4 , 'secret-number' ) #(1) @GreatAI . create @parameter ( 'positive_number' , validate = lambda n : n > 0 , disable_logging = True ) @use_model ( 'secret-number' , version = 'latest' , model_kwarg_name = 'secret' ) def add_number ( positive_number : int , secret : int ) -> int : log_metric ( 'log directly into the returned Trace' , positive_number * 2 ) return positive_number + secret assert add_number ( 1 ) . output == 5 Refer to the configuration page for specifying where to store your models.","title":"Complex example"},{"location":"how-to-guides/handle-training-data/","text":"How to manage training data # In order to simplify your training data management, great-ai provide two complementing approaches for inputting new data-points. Upload data # At the start of your experiments' first iteration, after you've gathered suitable samples for training, you can call great_ai.add_ground_truth . This automatically stores a timestamp and also allows you to assign tags to the data. Using these attributes, great_ai.query_ground_truth can be called to get a filtered view of the training data. Train-test-validation splits It is a best-practice to lock-away the test split of your data that is only used for the final quality assessment. This prevents you from accidentally training on it, or inadvertently tuning the model to have the highest accuracy metrics on the test split. This, of course, may lead to dubious results, hence, care must be taken to avoid it. With great_ai.add_ground_truth , there is an option to tag the samples with train , test , and validation randomly, following a predefined distribution. This happens as soon as they're written in the database. Later, these can be queried by providing the name of the appropriate tags. The nice thing about this is that the 'input-expected output' pairs are stored as traces. Thus, they behave exactly like regular prediction traces. from great_ai import add_ground_truth add_ground_truth ( [ 1 , 2 ], [ 'odd' , 'even' ], tags = 'my_tag' , train_split_ratio = 1 , #(1) test_split_ratio = 1 ) Note that the ratios don't have to add up to 1. They are just weights. There is also a validation_split_ratio which is 0 by default. >>> from great_ai import query_ground_truth >>> query_ground_truth ( 'my_tag' ) [ Trace [ str ]({ 'created' : '2022-07-12T18:36:12.825706' , 'exception' : None , 'feedback' : 'odd' , #(1) 'logged_values' : { 'input' : 1 }, #(2) 'models' : [], 'original_execution_time_ms' : 0.0 , 'output' : 'odd' , 'tags' : [ 'ground_truth' , 'test' , 'my_tag' ], #(3) 'trace_id' : '4fcf2ce6-a172-469d-94b2-874577655814' }), Trace [ str ]({ 'created' : '2022-07-12T18:36:12.825706' , 'exception' : None , 'feedback' : 'even' , 'logged_values' : { 'input' : 2 }, 'models' : [], 'original_execution_time_ms' : 0.0 , 'output' : 'even' , 'tags' : [ 'ground_truth' , 'train' , 'my_tag' ], 'trace_id' : 'abee0671-beb9-4284-8c3b-c65e5836ce38' })] Expected output. This can be also accessed through the .output property. The input value is stored here. Notice how ground_truth is always included as a tag when using great_ai.add_ground_truth . Get feedback # After the initial data gathering, end-to-end feedback can be also integrated into the dataset. The scaffolded REST API contains endpoints for managing traces and their feedbacks. When great_ai.query_ground_truth is executed, it implicitly filters for traces that have feedback. Therefore, both the ground_truth and the online traces that have received feedback are returned. No matter the origin of the data, it can be accessed using the same API. Remove clutter # Traces can be deleted either through the REST API or by calling great_ai.delete_ground_truth . The latter provides the same interface as great_ai.query_ground_truth except it deletes the matched points.","title":"How to manage training data"},{"location":"how-to-guides/handle-training-data/#how-to-manage-training-data","text":"In order to simplify your training data management, great-ai provide two complementing approaches for inputting new data-points.","title":"How to manage training data"},{"location":"how-to-guides/handle-training-data/#upload-data","text":"At the start of your experiments' first iteration, after you've gathered suitable samples for training, you can call great_ai.add_ground_truth . This automatically stores a timestamp and also allows you to assign tags to the data. Using these attributes, great_ai.query_ground_truth can be called to get a filtered view of the training data. Train-test-validation splits It is a best-practice to lock-away the test split of your data that is only used for the final quality assessment. This prevents you from accidentally training on it, or inadvertently tuning the model to have the highest accuracy metrics on the test split. This, of course, may lead to dubious results, hence, care must be taken to avoid it. With great_ai.add_ground_truth , there is an option to tag the samples with train , test , and validation randomly, following a predefined distribution. This happens as soon as they're written in the database. Later, these can be queried by providing the name of the appropriate tags. The nice thing about this is that the 'input-expected output' pairs are stored as traces. Thus, they behave exactly like regular prediction traces. from great_ai import add_ground_truth add_ground_truth ( [ 1 , 2 ], [ 'odd' , 'even' ], tags = 'my_tag' , train_split_ratio = 1 , #(1) test_split_ratio = 1 ) Note that the ratios don't have to add up to 1. They are just weights. There is also a validation_split_ratio which is 0 by default. >>> from great_ai import query_ground_truth >>> query_ground_truth ( 'my_tag' ) [ Trace [ str ]({ 'created' : '2022-07-12T18:36:12.825706' , 'exception' : None , 'feedback' : 'odd' , #(1) 'logged_values' : { 'input' : 1 }, #(2) 'models' : [], 'original_execution_time_ms' : 0.0 , 'output' : 'odd' , 'tags' : [ 'ground_truth' , 'test' , 'my_tag' ], #(3) 'trace_id' : '4fcf2ce6-a172-469d-94b2-874577655814' }), Trace [ str ]({ 'created' : '2022-07-12T18:36:12.825706' , 'exception' : None , 'feedback' : 'even' , 'logged_values' : { 'input' : 2 }, 'models' : [], 'original_execution_time_ms' : 0.0 , 'output' : 'even' , 'tags' : [ 'ground_truth' , 'train' , 'my_tag' ], 'trace_id' : 'abee0671-beb9-4284-8c3b-c65e5836ce38' })] Expected output. This can be also accessed through the .output property. The input value is stored here. Notice how ground_truth is always included as a tag when using great_ai.add_ground_truth .","title":"Upload data"},{"location":"how-to-guides/handle-training-data/#get-feedback","text":"After the initial data gathering, end-to-end feedback can be also integrated into the dataset. The scaffolded REST API contains endpoints for managing traces and their feedbacks. When great_ai.query_ground_truth is executed, it implicitly filters for traces that have feedback. Therefore, both the ground_truth and the online traces that have received feedback are returned. No matter the origin of the data, it can be accessed using the same API.","title":"Get feedback"},{"location":"how-to-guides/handle-training-data/#remove-clutter","text":"Traces can be deleted either through the REST API or by calling great_ai.delete_ground_truth . The latter provides the same interface as great_ai.query_ground_truth except it deletes the matched points.","title":"Remove clutter"},{"location":"how-to-guides/install/","text":"Installation guide # Provided you already have Python3 (and pip) installed, simply execute: pip install great-ai Python 3.8 or later is required. This will work on all major operating systems. Command-line tools # After installation, great-ai and large-file are available as commands. The former is required for deploying your application while the latter lets you manage models and datasets from your terminal. Snakes & kebabs The library is called great-ai , therefore, its command-line entrypoint is also called great-ai . However, Python module names cannot contain hyphens, that's why you have to import great_ai with an underscore. The great-ai CLI tool is also available as python3 -m great_ai . To help with the confusion, a CLI executable called great_ai (and large_file ) are also installed, thus, if you prefer, you can always refer to GreatAI using its underscored name variant ( great_ai ). Windows On Windows, you might encounter a similar warning from pip : WARNING: The scripts great-ai.exe, great_ai.exe, large-file.exe and large_file.exe are installed in 'C:\\Users\\...\\Scripts' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. This means that great-ai.exe and large-file.exe are not in your PATH . Either add their containing directory ('C:\\Users...\\Scripts' in this case) to your PATH or use python3 -m great_ai and python3 -m great_ai.large_file instead of the exe-s. Update # If you wish to update to the latest version execute: pip install --upgrade great-ai","title":"Installation guide"},{"location":"how-to-guides/install/#installation-guide","text":"Provided you already have Python3 (and pip) installed, simply execute: pip install great-ai Python 3.8 or later is required. This will work on all major operating systems.","title":"Installation guide"},{"location":"how-to-guides/install/#command-line-tools","text":"After installation, great-ai and large-file are available as commands. The former is required for deploying your application while the latter lets you manage models and datasets from your terminal. Snakes & kebabs The library is called great-ai , therefore, its command-line entrypoint is also called great-ai . However, Python module names cannot contain hyphens, that's why you have to import great_ai with an underscore. The great-ai CLI tool is also available as python3 -m great_ai . To help with the confusion, a CLI executable called great_ai (and large_file ) are also installed, thus, if you prefer, you can always refer to GreatAI using its underscored name variant ( great_ai ). Windows On Windows, you might encounter a similar warning from pip : WARNING: The scripts great-ai.exe, great_ai.exe, large-file.exe and large_file.exe are installed in 'C:\\Users\\...\\Scripts' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. This means that great-ai.exe and large-file.exe are not in your PATH . Either add their containing directory ('C:\\Users...\\Scripts' in this case) to your PATH or use python3 -m great_ai and python3 -m great_ai.large_file instead of the exe-s.","title":"Command-line tools"},{"location":"how-to-guides/install/#update","text":"If you wish to update to the latest version execute: pip install --upgrade great-ai","title":"Update"},{"location":"how-to-guides/large_file/","text":"How to use LargeFiles # The functions save_model and @use_model wrap LargeFile instances. Hence, besides configuring LargeFile , users have few reasons to use LargeFiles directly. Motivation # Oftentimes, especially when working with data-heavy applications, large files can proliferate in a repository. Version controlling them is an obvious next step, however, GitHub's git LFS implementation doesn't support deleting large files, making it easy for them to eat-up the LFS quota and explode the size of your repos. DVC is a viable alternative, however, it requires users to learn to use one more CLI tool. Using LargeFile-s directly (usually not needed) LargeFile doesn't require users to learn too much new. It is a nearly exact copy of the built-in open() function of Python with which users are certainly already familiar. Simple example # from great_ai.large_file import LargeFileS3 LargeFileS3 . configure_credentials ({ \"aws_region_name\" : \"your_region_like_eu-west-2\" , \"aws_access_key_id\" : \"YOUR_ACCESS_KEY_ID\" , \"aws_secret_access_key\" : \"YOUR_VERY_SECRET_ACCESS_KEY\" , \"large_files_bucket_name\" : \"create_a_bucket_and_put_its_name_here\" , }) # Creates a new version and deletes the older version # leaving the 3 most recently used intact with LargeFileS3 ( \"test.txt\" , \"w\" , keep_last_n = 3 ) as f : for i in range ( 100000 ): f . write ( 'test \\n ' ) # The latest version is returned by default # but an optional `version` keyword argument can be provided as well with LargeFileS3 ( \"test.txt\" , \"r\" ) as f : #(1) print ( f . readlines ()[ 0 ]) In this case, the latest version is already in the local cache, no download is required. More details # LargeFile behaves like an opened file (in the background it is a temp file after all). Binary reads and writes are supported along with the different keywords open() accepts . The local cache can be configured with these properties: LargeFileS3 . cache_path = Path ( '.cache' ) LargeFileS3 . max_cache_size = \"30 GB\" I only need a path # In case you only need a path to the (proxy of the) remote file, this pattern can be applied: path_to_model = LargeFileS3 ( \"folder-of-my-bert-model\" , version = 31 ) . get () This will first download the file/folder into your local cache folder. Then, it returns a Path object to the local version. Which can be turned into a string with str(path_to_model) . The same approach works for uploads: LargeFileS3 ( \"folder-of-my-bert-model\" ) . push ( 'path_to_local/folder_or_file' ) This way both regular files and folders can be handled. The uploaded file is called folder-of-my-bert-model , the local name is ignored. Lastly, all version of the remote object can be deleted by calling LargeFileS3(\"my-file\").delete() . It will still reside in your local cache afterwards; its deletion will happen next time your local cache has to be pruned. From the command-line # The main reason for using the large-file or python3 -m great_ai.large_file commands is to upload or download models from the terminal. For example, when building a docker image, it is best-practice to cache the referred models. Setup # Create an .ini file (or use ~/.aws/credentials ). It may look like this: aws_region_name = your_region_like_eu-west-2 aws_access_key_id = YOUR_ACCESS_KEY_ID aws_secret_access_key = YOUR_VERY_SECRET_ACCESS_KEY large_files_bucket_name = my_large_files Upload some files # large-file --backend s3 --secrets secrets.ini \\ --push my_first_file.json folder/my_second_file my_folder Only the filename is used as the S3 name, the rest of the path is ignored. Using MongoDB The possible values for --backend are s3 , mongo , and local . The latter doesn't need credentials, it only versions and stores your files in a local folder. MongoDB on the other hand requires a mongo_connection_string and a mongo_database to be specified. For storing large files, it uses the GridFS specification. Download some files to the local cache # This can be useful when building a Docker image for example. This way, the files can already reside inside the container and need not be downloaded later. large-file --backend s3 --secrets ~/.aws/credentials \\ --cache my_first_file.json:3 my_second_file my_folder:0 Versions may be specified by using : -s. Delete remote files # large-file --backend s3 --secrets ~/.aws/credentials \\ --delete my_first_file.json","title":"How to use LargeFiles"},{"location":"how-to-guides/large_file/#how-to-use-largefiles","text":"The functions save_model and @use_model wrap LargeFile instances. Hence, besides configuring LargeFile , users have few reasons to use LargeFiles directly.","title":"How to use LargeFiles"},{"location":"how-to-guides/large_file/#motivation","text":"Oftentimes, especially when working with data-heavy applications, large files can proliferate in a repository. Version controlling them is an obvious next step, however, GitHub's git LFS implementation doesn't support deleting large files, making it easy for them to eat-up the LFS quota and explode the size of your repos. DVC is a viable alternative, however, it requires users to learn to use one more CLI tool. Using LargeFile-s directly (usually not needed) LargeFile doesn't require users to learn too much new. It is a nearly exact copy of the built-in open() function of Python with which users are certainly already familiar.","title":"Motivation"},{"location":"how-to-guides/large_file/#simple-example","text":"from great_ai.large_file import LargeFileS3 LargeFileS3 . configure_credentials ({ \"aws_region_name\" : \"your_region_like_eu-west-2\" , \"aws_access_key_id\" : \"YOUR_ACCESS_KEY_ID\" , \"aws_secret_access_key\" : \"YOUR_VERY_SECRET_ACCESS_KEY\" , \"large_files_bucket_name\" : \"create_a_bucket_and_put_its_name_here\" , }) # Creates a new version and deletes the older version # leaving the 3 most recently used intact with LargeFileS3 ( \"test.txt\" , \"w\" , keep_last_n = 3 ) as f : for i in range ( 100000 ): f . write ( 'test \\n ' ) # The latest version is returned by default # but an optional `version` keyword argument can be provided as well with LargeFileS3 ( \"test.txt\" , \"r\" ) as f : #(1) print ( f . readlines ()[ 0 ]) In this case, the latest version is already in the local cache, no download is required.","title":"Simple example"},{"location":"how-to-guides/large_file/#more-details","text":"LargeFile behaves like an opened file (in the background it is a temp file after all). Binary reads and writes are supported along with the different keywords open() accepts . The local cache can be configured with these properties: LargeFileS3 . cache_path = Path ( '.cache' ) LargeFileS3 . max_cache_size = \"30 GB\"","title":"More details"},{"location":"how-to-guides/large_file/#i-only-need-a-path","text":"In case you only need a path to the (proxy of the) remote file, this pattern can be applied: path_to_model = LargeFileS3 ( \"folder-of-my-bert-model\" , version = 31 ) . get () This will first download the file/folder into your local cache folder. Then, it returns a Path object to the local version. Which can be turned into a string with str(path_to_model) . The same approach works for uploads: LargeFileS3 ( \"folder-of-my-bert-model\" ) . push ( 'path_to_local/folder_or_file' ) This way both regular files and folders can be handled. The uploaded file is called folder-of-my-bert-model , the local name is ignored. Lastly, all version of the remote object can be deleted by calling LargeFileS3(\"my-file\").delete() . It will still reside in your local cache afterwards; its deletion will happen next time your local cache has to be pruned.","title":"I only need a path"},{"location":"how-to-guides/large_file/#from-the-command-line","text":"The main reason for using the large-file or python3 -m great_ai.large_file commands is to upload or download models from the terminal. For example, when building a docker image, it is best-practice to cache the referred models.","title":"From the command-line"},{"location":"how-to-guides/large_file/#setup","text":"Create an .ini file (or use ~/.aws/credentials ). It may look like this: aws_region_name = your_region_like_eu-west-2 aws_access_key_id = YOUR_ACCESS_KEY_ID aws_secret_access_key = YOUR_VERY_SECRET_ACCESS_KEY large_files_bucket_name = my_large_files","title":"Setup"},{"location":"how-to-guides/large_file/#upload-some-files","text":"large-file --backend s3 --secrets secrets.ini \\ --push my_first_file.json folder/my_second_file my_folder Only the filename is used as the S3 name, the rest of the path is ignored. Using MongoDB The possible values for --backend are s3 , mongo , and local . The latter doesn't need credentials, it only versions and stores your files in a local folder. MongoDB on the other hand requires a mongo_connection_string and a mongo_database to be specified. For storing large files, it uses the GridFS specification.","title":"Upload some files"},{"location":"how-to-guides/large_file/#download-some-files-to-the-local-cache","text":"This can be useful when building a Docker image for example. This way, the files can already reside inside the container and need not be downloaded later. large-file --backend s3 --secrets ~/.aws/credentials \\ --cache my_first_file.json:3 my_second_file my_folder:0 Versions may be specified by using : -s.","title":"Download some files to the local cache"},{"location":"how-to-guides/large_file/#delete-remote-files","text":"large-file --backend s3 --secrets ~/.aws/credentials \\ --delete my_first_file.json","title":"Delete remote files"},{"location":"how-to-guides/use-service/","text":"How to perform prediction with GreatAI # After creating a GreatAI service by wrapping your prediction function, and optionally configuring it , it's time to do some prediction. Let's take the following example: greeter.py from great_ai import GreatAI @GreatAI . create def greeter ( your_name : str ) -> str : return f 'Hi { your_name } !' One-off prediction # Even though greeter is now an instance of GreatAI , you can continue using it as a regular function. >>> greeter ( 'Bob' ) Trace [ str ]({ 'created' : '2022-07-11T14:31:46.183764' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 3 , 'arg:your_name:value' : 'Bob' }, 'models' : [], 'original_execution_time_ms' : 0.0381 , 'output' : 'Hi Bob!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : '7c284fd7-7f0d-4464-b5f8-3ef126df34af' }) As you can see, the original return value is wrapped in a Trace object (which is also persisted in your database of choice). You can access the original value under the output property. Online prediction # Likely, the main way you would like to expose your model is through an HTTP API. @GreatAI.create scaffolds many REST API endpoints for your model and creates a FastAPI app available under GreatAI.app . This can be served using uvicorn or any other ASGI server . Since most ML code lives in Jupyter notebooks, therefore, deploying a notebook containing the inference function is supported. To achieve this, uvicorn is wrapped by the great-ai command-line utility which \u2014 among others \u2014 takes care of feeding a notebook into uvicorn . It also supports auto-reloading. In development # great-ai greeter.py Success Your model is accessible at localhost:6060 . Some configuration options are also supported. great-ai greeter.py --port 8000 --host 127 .0.0.1 --timeout_keep_alive 10 More options For more options (but no Notebook support), simply use uvicorn for starting your app (available at greeter.app ). In production # There are three main approaches for deploying a GreatAI service. Manual deployment # The app is run in production-mode if the value of the ENVIRONMENT environment variable is set to production . ENVIRONMENT = production great-ai greeter.py Simply run ENVIRONMENT=production great-ai deploy.ipynb in the command-line of a production machine. This is the crudest approach; however, it might be fitting for some contexts. Containerised deployment # Run the notebook directly in a container or create a service for it using your favourite container orchestrator. docker run -p 6060 :6060 --volume ` pwd ` :/app --rm \\ schmelczera/great-ai deploy.ipynb You can replace pwd with the path to your code's folder. Use a Platform-as-a-Service # Similar to the previous approach, your code will run in a container. However, instead of manually managing it, you can just choose from a plethora of PaaS providers (such as AWS ECS , DO App platform , MLEM , Streamlit ) that take a Docker image as a source and handle the rest of the deployment. To this end, you can also create a custom Docker image. It is especially useful if you have third-party dependencies, such as PyTorch or TensorFlow . FROM schmelczera/great-ai:latest # Remove this block if you don't have a requirements.txt COPY requirements.txt ./ RUN pip install --no-cache-dir --requirement requirements.txt # If you store your models in S3 or GridFS, it may be a # good idea to cache them in the image so that you don't # have to download it each time a container starts RUN large-file --backend s3 --secrets s3.ini --cache my-domain-predictor # Add you application code to the image COPY . . # The default ENTRYPOINT is great-ai, specify its argument using CMD CMD [ \"deploy.ipynb\" ] Batch prediction # Processing larger amounts of data on a single machine is made easy by the GreatAI 's process_batch method. This relies on multiprocessing ( parallel_map ) to take full advantage of all available CPU-cores. >>> greeter . process_batch ([ 'Alice' , 'Bob' ]) [ Trace [ str ]({ 'created' : '2022-07-11T14:36:37.119183' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 5 , 'arg:your_name:value' : 'Alice' }, 'models' : [], 'original_execution_time_ms' : 0.1251 , 'output' : 'Hi Alice!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : '90ffa15f-e839-41c4-8e7a-3211168bc138' }), Trace [ str ]({ 'created' : '2022-07-11T14:36:37.166659' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 3 , 'arg:your_name:value' : 'Bob' }, 'models' : [], 'original_execution_time_ms' : 0.0571 , 'output' : 'Hi Bob!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : 'f48e94c7-0815-48b3-a864-41349d3dae84' })]","title":"How to perform prediction with GreatAI"},{"location":"how-to-guides/use-service/#how-to-perform-prediction-with-greatai","text":"After creating a GreatAI service by wrapping your prediction function, and optionally configuring it , it's time to do some prediction. Let's take the following example: greeter.py from great_ai import GreatAI @GreatAI . create def greeter ( your_name : str ) -> str : return f 'Hi { your_name } !'","title":"How to perform prediction with GreatAI"},{"location":"how-to-guides/use-service/#one-off-prediction","text":"Even though greeter is now an instance of GreatAI , you can continue using it as a regular function. >>> greeter ( 'Bob' ) Trace [ str ]({ 'created' : '2022-07-11T14:31:46.183764' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 3 , 'arg:your_name:value' : 'Bob' }, 'models' : [], 'original_execution_time_ms' : 0.0381 , 'output' : 'Hi Bob!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : '7c284fd7-7f0d-4464-b5f8-3ef126df34af' }) As you can see, the original return value is wrapped in a Trace object (which is also persisted in your database of choice). You can access the original value under the output property.","title":"One-off prediction"},{"location":"how-to-guides/use-service/#online-prediction","text":"Likely, the main way you would like to expose your model is through an HTTP API. @GreatAI.create scaffolds many REST API endpoints for your model and creates a FastAPI app available under GreatAI.app . This can be served using uvicorn or any other ASGI server . Since most ML code lives in Jupyter notebooks, therefore, deploying a notebook containing the inference function is supported. To achieve this, uvicorn is wrapped by the great-ai command-line utility which \u2014 among others \u2014 takes care of feeding a notebook into uvicorn . It also supports auto-reloading.","title":"Online prediction"},{"location":"how-to-guides/use-service/#in-development","text":"great-ai greeter.py Success Your model is accessible at localhost:6060 . Some configuration options are also supported. great-ai greeter.py --port 8000 --host 127 .0.0.1 --timeout_keep_alive 10 More options For more options (but no Notebook support), simply use uvicorn for starting your app (available at greeter.app ).","title":"In development"},{"location":"how-to-guides/use-service/#in-production","text":"There are three main approaches for deploying a GreatAI service.","title":"In production"},{"location":"how-to-guides/use-service/#manual-deployment","text":"The app is run in production-mode if the value of the ENVIRONMENT environment variable is set to production . ENVIRONMENT = production great-ai greeter.py Simply run ENVIRONMENT=production great-ai deploy.ipynb in the command-line of a production machine. This is the crudest approach; however, it might be fitting for some contexts.","title":"Manual deployment"},{"location":"how-to-guides/use-service/#containerised-deployment","text":"Run the notebook directly in a container or create a service for it using your favourite container orchestrator. docker run -p 6060 :6060 --volume ` pwd ` :/app --rm \\ schmelczera/great-ai deploy.ipynb You can replace pwd with the path to your code's folder.","title":"Containerised deployment"},{"location":"how-to-guides/use-service/#use-a-platform-as-a-service","text":"Similar to the previous approach, your code will run in a container. However, instead of manually managing it, you can just choose from a plethora of PaaS providers (such as AWS ECS , DO App platform , MLEM , Streamlit ) that take a Docker image as a source and handle the rest of the deployment. To this end, you can also create a custom Docker image. It is especially useful if you have third-party dependencies, such as PyTorch or TensorFlow . FROM schmelczera/great-ai:latest # Remove this block if you don't have a requirements.txt COPY requirements.txt ./ RUN pip install --no-cache-dir --requirement requirements.txt # If you store your models in S3 or GridFS, it may be a # good idea to cache them in the image so that you don't # have to download it each time a container starts RUN large-file --backend s3 --secrets s3.ini --cache my-domain-predictor # Add you application code to the image COPY . . # The default ENTRYPOINT is great-ai, specify its argument using CMD CMD [ \"deploy.ipynb\" ]","title":"Use a Platform-as-a-Service"},{"location":"how-to-guides/use-service/#batch-prediction","text":"Processing larger amounts of data on a single machine is made easy by the GreatAI 's process_batch method. This relies on multiprocessing ( parallel_map ) to take full advantage of all available CPU-cores. >>> greeter . process_batch ([ 'Alice' , 'Bob' ]) [ Trace [ str ]({ 'created' : '2022-07-11T14:36:37.119183' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 5 , 'arg:your_name:value' : 'Alice' }, 'models' : [], 'original_execution_time_ms' : 0.1251 , 'output' : 'Hi Alice!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : '90ffa15f-e839-41c4-8e7a-3211168bc138' }), Trace [ str ]({ 'created' : '2022-07-11T14:36:37.166659' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 3 , 'arg:your_name:value' : 'Bob' }, 'models' : [], 'original_execution_time_ms' : 0.0571 , 'output' : 'Hi Bob!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : 'f48e94c7-0815-48b3-a864-41349d3dae84' })]","title":"Batch prediction"},{"location":"reference/","text":"GreatAI reference # from great_ai import * Core # GreatAI # Bases: Generic [ T , V ] Wrapper for a prediction function providing the implementation of SE4ML best-practices. Provides caching (with argument freezing), a TracingContext during execution, the scaffolding of HTTP endpoints using FastAPI and a dashboard using Dash. Supports wrapping async and synchronous functions while also maintaining correct typing. Attributes: Name Type Description app FastAPI instance wrapping the scaffolded endpoints and the Dash app. version SemVer derived from the app's version and the model names and versions registered through use_model. Source code in great_ai/deploy/great_ai.py 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 class GreatAI ( Generic [ T , V ]): \"\"\"Wrapper for a prediction function providing the implementation of SE4ML best-practices. Provides caching (with argument freezing), a TracingContext during execution, the scaffolding of HTTP endpoints using FastAPI and a dashboard using Dash. Supports wrapping async and synchronous functions while also maintaining correct typing. Attributes: app: FastAPI instance wrapping the scaffolded endpoints and the Dash app. version: SemVer derived from the app's version and the model names and versions registered through use_model. \"\"\" __name__ : str # help for MyPy __doc__ : str # help for MyPy def __init__ ( self , func : Callable [ ... , Union [ V , Awaitable [ V ]]], ): \"\"\"Do not call this function directly, use GreatAI.create instead.\"\"\" func = automatically_decorate_parameters ( func ) get_function_metadata_store ( func ) . is_finalised = True self . _cached_func = self . _get_cached_traced_function ( func ) self . _wrapped_func = wraps ( func )( freeze_arguments ( self . _cached_func )) wraps ( func )( self ) self . __doc__ = ( f \"GreatAI wrapper for interacting with the ` { self . __name__ } ` \" + f \"function. \\n\\n { dedent ( self . __doc__ or '' ) } \" ) self . version = str ( get_context () . version ) flat_model_versions = \".\" . join ( f \" { k } -v { v } \" for k , v in model_versions ) if flat_model_versions : self . version += f \"+ { flat_model_versions } \" self . app = FastAPI ( title = snake_case_to_text ( self . __name__ ), version = self . version , description = self . __doc__ + f \" \\n\\n Find out more in the [dashboard]( { DASHBOARD_PATH } ).\" , docs_url = None , redoc_url = None , ) self . _bootstrap_rest_api () @overload @staticmethod def create ( # type: ignore # Overloaded function signatures 1 and 2 overlap with incompatible return types # https://github.com/python/mypy/issues/12759 func : Callable [ ... , Awaitable [ V ]], ) -> \"GreatAI[Awaitable[Trace[V]], V]\" : ... @overload @staticmethod def create ( func : Callable [ ... , V ], ) -> \"GreatAI[Trace[V], V]\" : ... @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a [@parameter][great_ai.parameter] or [@use_model][great_ai.use_model] decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see [great_ai.configure][]. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , ) def __call__ ( self , * args : Any , ** kwargs : Any ) -> T : return self . _wrapped_func ( * args , ** kwargs ) @overload def process_batch ( self , batch : Sequence [ Tuple ], * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ True ], do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... @overload def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ False ] = ... , do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : bool = False , do_not_persist_traces : bool = False , ) -> List [ Trace [ V ]]: \"\"\"Map the wrapped function over a list of input_values (`batch`). A wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] providing type-safety and a progressbar through tqdm. Args: batch: A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set `unpack_arguments=True`. concurrency: Number of processes to start. Don't set it too much higher than the number of available CPU cores. unpack_arguments: Expect a list of tuples and unpack the tuples before giving them to the wrapped function. do_not_persist_traces: Don't save the traces in the database. Useful for evaluations run part of the CI. \"\"\" wrapped_function = self . _wrapped_func def inner ( value : Any ) -> T : return ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ) async def inner_async ( value : Any ) -> T : return await cast ( Awaitable , ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ), ) return list ( tqdm ( parallel_map ( inner_async if get_function_metadata_store ( self ) . is_asynchronous else inner , batch , concurrency = concurrency , ), total = len ( batch ), ) ) @staticmethod def _get_cached_traced_function ( func : Callable [ ... , Union [ V , Awaitable [ V ]]] ) -> Callable [ ... , T ]: @lru_cache ( maxsize = get_context () . prediction_cache_size ) def func_in_tracing_context_sync ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = func ( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) @alru_cache ( maxsize = get_context () . prediction_cache_size ) async def func_in_tracing_context_async ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = await cast ( Callable [ ... , Awaitable ], func )( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) return cast ( Callable [ ... , T ], ( func_in_tracing_context_async if get_function_metadata_store ( func ) . is_asynchronous else func_in_tracing_context_sync ), ) def _bootstrap_rest_api ( self , ) -> None : route_config = get_context () . route_config if route_config . prediction_endpoint_enabled : bootstrap_prediction_endpoint ( self . app , self . _wrapped_func ) if route_config . docs_endpoints_enabled : bootstrap_docs_endpoints ( self . app ) if route_config . dashboard_enabled : bootstrap_dashboard ( self . app , function_name = self . __name__ , documentation = self . __doc__ , ) if route_config . trace_endpoints_enabled : bootstrap_trace_endpoints ( self . app ) if route_config . feedback_endpoints_enabled : bootstrap_feedback_endpoints ( self . app ) if route_config . meta_endpoints_enabled : bootstrap_meta_endpoints ( self . app , self . _cached_func , ApiMetadata ( name = self . __name__ , version = self . version , documentation = self . __doc__ , configuration = get_context () . to_flat_dict (), ), ) create ( func ) staticmethod # Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a @parameter or @use_model decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the .output property. For configuration options, see great_ai.configure . Examples: >>> @GreatAI . create ... def my_function ( a ): ... return a + 2 >>> my_function ( 3 ) . output 5 >>> @GreatAI . create ... def my_function ( a : int ) -> int : ... return a + 2 >>> my_function ( 3 ) Trace[int]... >>> my_function ( '3' ) . output Traceback (most recent call last): ... TypeError : type of a must be int; got str instead Parameters: Name Type Description Default func Union [ Callable [..., Awaitable [ V ]], Callable [..., V ]] The prediction function that needs to be decorated. required Returns: Type Description Union [ GreatAI[Awaitable[Trace[V]], V] , GreatAI[Trace[V], V] ] A GreatAI instance wrapping func . Source code in great_ai/deploy/great_ai.py 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a [@parameter][great_ai.parameter] or [@use_model][great_ai.use_model] decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see [great_ai.configure][]. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , ) process_batch ( batch , * , concurrency = None , unpack_arguments = False , do_not_persist_traces = False ) # Map the wrapped function over a list of input_values ( batch ). A wrapper over parallel_map providing type-safety and a progressbar through tqdm. Parameters: Name Type Description Default batch Sequence A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set unpack_arguments=True . required concurrency Optional [ int ] Number of processes to start. Don't set it too much higher than the number of available CPU cores. None unpack_arguments bool Expect a list of tuples and unpack the tuples before giving them to the wrapped function. False do_not_persist_traces bool Don't save the traces in the database. Useful for evaluations run part of the CI. False Source code in great_ai/deploy/great_ai.py 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : bool = False , do_not_persist_traces : bool = False , ) -> List [ Trace [ V ]]: \"\"\"Map the wrapped function over a list of input_values (`batch`). A wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] providing type-safety and a progressbar through tqdm. Args: batch: A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set `unpack_arguments=True`. concurrency: Number of processes to start. Don't set it too much higher than the number of available CPU cores. unpack_arguments: Expect a list of tuples and unpack the tuples before giving them to the wrapped function. do_not_persist_traces: Don't save the traces in the database. Useful for evaluations run part of the CI. \"\"\" wrapped_function = self . _wrapped_func def inner ( value : Any ) -> T : return ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ) async def inner_async ( value : Any ) -> T : return await cast ( Awaitable , ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ), ) return list ( tqdm ( parallel_map ( inner_async if get_function_metadata_store ( self ) . is_asynchronous else inner , batch , concurrency = concurrency , ), total = len ( batch ), ) ) configure ( * , version = '0.0.1' , log_level = DEBUG , seed = 42 , tracing_database_factory = None , large_file_implementation = None , should_log_exception_stack = None , prediction_cache_size = 512 , disable_se4ml_banner = False , dashboard_table_size = 50 , route_config = RouteConfig ()) # Set the global configuration used by the great-ai library. You must call configure before calling (or decorating with) any other great-ai function. If tracing_database_factory or large_file_implementation is not specified, their default value is determined based on which TracingDatabase and LargeFile has been configured (e.g.: LargeFileS3.configure_credentials_from_file('s3.ini')), or whether there is any file named s3.ini or mongo.ini in the working directory. Examples: >>> configure ( prediction_cache_size = 0 ) Parameters: Name Type Description Default version Union [ int , str ] The version of your application (using SemVer is recommended). '0.0.1' log_level int Set the default logging level of logging . DEBUG seed int Set seed of random (and numpy if installed) for reproducibility. 42 tracing_database_factory Optional [ Type [ TracingDatabaseDriver ]] Specify a different TracingDatabaseDriver than the one already configured. None large_file_implementation Optional [ Type [ LargeFileBase ]] Specify a different LargeFile than the one already configured. None should_log_exception_stack Optional [ bool ] Log the traces of unhandled exceptions. None prediction_cache_size int Size of the LRU cache applied over the prediction functions. 512 disable_se4ml_banner bool Turn off the warning about the importance of SE4ML best- practices. False dashboard_table_size int Number of rows to display in the dashboard's table. 50 route_config RouteConfig Enable or disable specific HTTP API endpoints. RouteConfig() Source code in great_ai/context.py 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 def configure ( * , version : Union [ int , str ] = \"0.0.1\" , log_level : int = DEBUG , seed : int = 42 , tracing_database_factory : Optional [ Type [ TracingDatabaseDriver ]] = None , large_file_implementation : Optional [ Type [ LargeFileBase ]] = None , should_log_exception_stack : Optional [ bool ] = None , prediction_cache_size : int = 512 , disable_se4ml_banner : bool = False , dashboard_table_size : int = 50 , route_config : RouteConfig = RouteConfig (), ) -> None : \"\"\"Set the global configuration used by the great-ai library. You must call `configure` before calling (or decorating with) any other great-ai function. If `tracing_database_factory` or `large_file_implementation` is not specified, their default value is determined based on which TracingDatabase and LargeFile has been configured (e.g.: LargeFileS3.configure_credentials_from_file('s3.ini')), or whether there is any file named s3.ini or mongo.ini in the working directory. Examples: >>> configure(prediction_cache_size=0) Arguments: version: The version of your application (using SemVer is recommended). log_level: Set the default logging level of `logging`. seed: Set seed of `random` (and `numpy` if installed) for reproducibility. tracing_database_factory: Specify a different TracingDatabaseDriver than the one already configured. large_file_implementation: Specify a different LargeFile than the one already configured. should_log_exception_stack: Log the traces of unhandled exceptions. prediction_cache_size: Size of the LRU cache applied over the prediction functions. disable_se4ml_banner: Turn off the warning about the importance of SE4ML best- practices. dashboard_table_size: Number of rows to display in the dashboard's table. route_config: Enable or disable specific HTTP API endpoints. \"\"\" global _context logger = get_logger ( \"great_ai\" , level = log_level ) if _context is not None : logger . error ( \"Configuration has been already initialised, overwriting. \\n \" + \"Make sure to call `configure()` before importing your application code.\" ) is_production = _is_in_production_mode ( logger = logger ) _set_seed ( seed ) tracing_database_factory = _initialize_tracing_database ( tracing_database_factory , logger = logger ) tracing_database = tracing_database_factory () if not tracing_database . is_production_ready : message = f \"\"\"The selected tracing database ( { tracing_database_factory . __name__ } ) is not recommended for production\"\"\" if is_production : logger . error ( message ) else : logger . warning ( message ) _context = Context ( version = version , tracing_database = tracing_database , large_file_implementation = _initialize_large_file ( large_file_implementation , logger = logger ), is_production = is_production , logger = logger , should_log_exception_stack = not is_production if should_log_exception_stack is None else should_log_exception_stack , prediction_cache_size = prediction_cache_size , dashboard_table_size = dashboard_table_size , route_config = route_config , ) logger . info ( f \"GreatAI (v { __version__ } ): configured \u2705\" ) for k , v in get_context () . to_flat_dict () . items (): logger . info ( f \" { LIST_ITEM_PREFIX }{ k } : { v } \" ) if not is_production and not disable_se4ml_banner : logger . warning ( \"You still need to check whether you follow all best practices before \" \"trusting your deployment.\" ) logger . warning ( f \"> Find out more at { SE4ML_WEBSITE } \" ) save_model ( model , key , * , keep_last_n = None ) # Save (and optionally serialise) a model in order to use by use_model . The model can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case model is an object, it is serialised using dill before uploading it. Examples: >>> from great_ai import use_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Parameters: Name Type Description Default model Union [ Path , str , object ] The object or path to be uploaded. required key str The model's name. required keep_last_n Optional [ int ] If specified, remove old models and only keep the latest n. Directly passed to LargeFile. None Returns: Type Description str The key and version of the saved model separated by a colon. Example: \"key:version\" Source code in great_ai/models/save_model.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 def save_model ( model : Union [ Path , str , object ], key : str , * , keep_last_n : Optional [ int ] = None ) -> str : \"\"\"Save (and optionally serialise) a model in order to use by `use_model`. The `model` can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case `model` is an object, it is serialised using `dill` before uploading it. Examples: >>> from great_ai import use_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: model: The object or path to be uploaded. key: The model's name. keep_last_n: If specified, remove old models and only keep the latest n. Directly passed to LargeFile. Returns: The key and version of the saved model separated by a colon. Example: \"key:version\" \"\"\" file = get_context () . large_file_implementation ( name = key , mode = \"wb\" , keep_last_n = keep_last_n ) if isinstance ( model , Path ) or isinstance ( model , str ): file . push ( model ) else : with file as f : dump ( model , f ) get_context () . logger . info ( f \"Model { key } uploaded with version { file . version } \" ) return f \" { key } : { file . version } \" use_model ( key , * , version = 'latest' , model_kwarg_name = 'model' ) # Inject a model into a function. Load a model specified by key and version using the currently active LargeFile implementation. If it's a single object, it is deserialised using dill . If it's a directory of files, a pathlib.Path instance is given. By default, the function's model parameter is replaced by the loaded model. This can be customised by changing model_kwarg_name . Multiple models can be loaded by decorating the same function with use_model multiple times. Examples: >>> from great_ai import save_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Args: key: The model's name as stored by the LargeFile implementation. version: The model's version as stored by the LargeFile implementation. model_kwarg_name: the parameter to use for injecting the loaded model Returns: A decorator for model injection. Source code in great_ai/models/use_model.py 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 def use_model ( key : str , * , version : Union [ int , Literal [ \"latest\" ]] = \"latest\" , model_kwarg_name : str = \"model\" , ) -> Callable [[ F ], F ]: \"\"\"Inject a model into a function. Load a model specified by `key` and `version` using the currently active `LargeFile` implementation. If it's a single object, it is deserialised using `dill`. If it's a directory of files, a `pathlib.Path` instance is given. By default, the function's `model` parameter is replaced by the loaded model. This can be customised by changing `model_kwarg_name`. Multiple models can be loaded by decorating the same function with `use_model` multiple times. Examples: >>> from great_ai import save_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: key: The model's name as stored by the LargeFile implementation. version: The model's version as stored by the LargeFile implementation. model_kwarg_name: the parameter to use for injecting the loaded model Returns: A decorator for model injection. \"\"\" assert ( isinstance ( version , int ) or version == \"latest\" ), \"Only integers or the string literal `latest` is allowed as a version\" model , actual_version = _load_model ( key = key , version = None if version == \"latest\" else version , ) def decorator ( func : F ) -> F : assert_function_is_not_finalised ( func ) store = get_function_metadata_store ( func ) store . model_parameter_names . append ( model_kwarg_name ) @wraps ( func ) def wrapper ( * args : List [ Any ], ** kwargs : Dict [ str , Any ]) -> Any : tracing_context = TracingContext . get_current_tracing_context () if tracing_context : tracing_context . log_model ( Model ( key = key , version = actual_version )) return func ( * args , ** kwargs , ** { model_kwarg_name : model }) return cast ( F , wrapper ) return decorator parameter ( parameter_name , * , validate = lambda _ : True , disable_logging = False ) # Control the validation and logging of function parameters. Examples: >>> @parameter ( 'a' ) ... def my_function ( a : int ): ... return a + 2 >>> my_function ( 4 ) 6 >>> my_function ( '3' ) Traceback (most recent call last): ... TypeError : type of a must be int; got str instead >>> @parameter ( 'positive_a' , validate = lambda v : v > 0 ) ... def my_function ( positive_a : int ): ... return a + 2 >>> my_function ( - 1 ) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError : ... Parameters: Name Type Description Default parameter_name str Name of parameter to consider required validate Callable [[ Any ], bool ] Optional validate to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. lambda _: True disable_logging bool Do not save the value in any active TracingContext. False Returns: Type Description Callable [[ F ], F ] A decorator for argument validation. Source code in great_ai/parameters/parameter.py 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 def parameter ( parameter_name : str , * , validate : Callable [[ Any ], bool ] = lambda _ : True , disable_logging : bool = False , ) -> Callable [[ F ], F ]: \"\"\"Control the validation and logging of function parameters. Examples: >>> @parameter('a') ... def my_function(a: int): ... return a + 2 >>> my_function(4) 6 >>> my_function('3') Traceback (most recent call last): ... TypeError: type of a must be int; got str instead >>> @parameter('positive_a', validate=lambda v: v > 0) ... def my_function(positive_a: int): ... return a + 2 >>> my_function(-1) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError: ... Args: parameter_name: Name of parameter to consider validate: Optional validate to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. disable_logging: Do not save the value in any active TracingContext. Returns: A decorator for argument validation. \"\"\" def decorator ( func : F ) -> F : get_function_metadata_store ( func ) . input_parameter_names . append ( parameter_name ) assert_function_is_not_finalised ( func ) actual_name = f \"arg: { parameter_name } \" @wraps ( func ) def wrapper ( * args : Any , ** kwargs : Dict [ str , Any ]) -> Any : arguments = get_arguments ( func , args , kwargs ) argument = arguments . get ( parameter_name ) expected_type = func . __annotations__ . get ( parameter_name ) if expected_type is not None : check_type ( parameter_name , argument , expected_type ) if not validate ( argument ): raise ArgumentValidationError ( f \"\"\"Argument { parameter_name } in { func . __name__ } did not pass validation\"\"\" ) context = TracingContext . get_current_tracing_context () if context and not disable_logging : context . log_value ( name = f \" { actual_name } :value\" , value = argument ) if isinstance ( argument , str ): context . log_value ( name = f \" { actual_name } :length\" , value = len ( argument )) return func ( * args , ** kwargs ) return cast ( F , wrapper ) return decorator log_metric ( argument_name , value ) # Source code in great_ai/parameters/log_metric.py 8 9 10 11 12 13 14 15 def log_metric ( argument_name : str , value : Any ) -> None : tracing_context = TracingContext . get_current_tracing_context () caller = inspect . stack ()[ 1 ] . function actual_name = f \"metric: { caller } : { argument_name } \" if tracing_context : tracing_context . log_value ( name = actual_name , value = value ) get_context () . logger . info ( f \" { actual_name } = { value } \" ) Remote calls # call_remote_great_ai ( base_uri , data , retry_count = 4 , timeout_in_seconds = 300 , model_class = None ) # Communicate with a GreatAI object through an HTTP request. Wrapper over call_remote_great_ai_async making it synchronous. For more info, see call_remote_great_ai_async . Source code in great_ai/remote/call_remote_great_ai.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 def call_remote_great_ai ( base_uri : str , data : Mapping [ str , Any ], retry_count : int = 4 , timeout_in_seconds : Optional [ int ] = 300 , model_class : Optional [ Type [ T ]] = None , ) -> Trace [ T ]: \"\"\"Communicate with a GreatAI object through an HTTP request. Wrapper over `call_remote_great_ai_async` making it synchronous. For more info, see `call_remote_great_ai_async`. \"\"\" try : asyncio . get_running_loop () raise Exception ( f \"Already running in an event loop, you have to call ` { call_remote_great_ai_async . __name__ } `\" ) except RuntimeError : pass future = call_remote_great_ai_async ( base_uri = base_uri , data = data , retry_count = retry_count , timeout_in_seconds = timeout_in_seconds , model_class = model_class , ) return asyncio . run ( future ) call_remote_great_ai_async ( base_uri , data , retry_count = 4 , timeout_in_seconds = 300 , model_class = None ) async # Communicate with a GreatAI object through an HTTP request. Send a POST request using httpx to implement a remote call. Error-handling and retries are provided by httpx. The return value is inflated into a Trace. If model_class is specified, the original output is deserialised. Parameters: Name Type Description Default base_uri str Address of the remote instance, example: 'http://localhost:6060' required data Mapping [ str , Any ] The input sent as a json to the remote instance. required retry_count int Retry on any HTTP communication failure. 4 timeout_in_seconds Optional [ int ] Overall permissable max length of the request. None means no timeout. 300 model_class Optional [ Type [ T ]] A subtype of BaseModel to be used for deserialising the .output of the trace. None Source code in great_ai/remote/call_remote_great_ai_async.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 async def call_remote_great_ai_async ( base_uri : str , data : Mapping [ str , Any ], retry_count : int = 4 , timeout_in_seconds : Optional [ int ] = 300 , model_class : Optional [ Type [ T ]] = None , ) -> Trace [ T ]: \"\"\"Communicate with a GreatAI object through an HTTP request. Send a POST request using [httpx](https://www.python-httpx.org/) to implement a remote call. Error-handling and retries are provided by httpx. The return value is inflated into a Trace. If `model_class` is specified, the original output is deserialised. Args: base_uri: Address of the remote instance, example: 'http://localhost:6060' data: The input sent as a json to the remote instance. retry_count: Retry on any HTTP communication failure. timeout_in_seconds: Overall permissable max length of the request. `None` means no timeout. model_class: A subtype of BaseModel to be used for deserialising the `.output` of the trace. \"\"\" if base_uri . endswith ( \"/\" ): base_uri = base_uri [: - 1 ] if not base_uri . endswith ( \"/predict\" ): base_uri = f \" { base_uri } /predict\" transport = httpx . AsyncHTTPTransport ( retries = retry_count ) try : async with httpx . AsyncClient ( transport = transport , timeout = timeout_in_seconds ) as client : response = await client . post ( base_uri , json = data ) try : response . raise_for_status () except Exception : raise RemoteCallError ( f \"Unexpected status code, reason: { response . text } \" ) except Exception as e : raise RemoteCallError from e try : trace = response . json () except Exception : raise RemoteCallError ( f \"JSON parsing failed { response . text } \" , ) try : if model_class is not None : trace [ \"output\" ] = model_class . parse_obj ( trace [ \"output\" ]) return Trace . parse_obj ( trace ) except Exception : raise RemoteCallError ( \"Could not parse Trace\" ) Ground-truth # add_ground_truth ( inputs , expected_outputs , * , tags = [], train_split_ratio = 1 , test_split_ratio = 0 , validation_split_ratio = 0 ) # Add training data (with optional train-test splitting). Add and tag data-points, wrap them into traces. The inputs are available via the .input property, while expected_outputs under both the .output and .feedback properties. All generated traces are tagged with ground_truth by default. Additional tags can be also provided. Using the split_ratio arguments, tags can be given randomly with a user-defined distribution. Only the ratio of the splits matter, they don't have to add up to 1. Examples: >>> add_ground_truth ( ... [ 1 , 2 , 3 ], ... [ 'odd' , 'even' , 'odd' ], ... tags = 'my_tag' , ... train_split_ratio = 1 , ... test_split_ratio = 1 , ... validation_split_ratio = 0.5 , ... ) >>> add_ground_truth ( ... [ 1 , 2 ], ... [ 'odd' , 'even' , 'odd' ], ... tags = 'my_tag' , ... train_split_ratio = 1 , ... test_split_ratio = 1 , ... validation_split_ratio = 0.5 , ... ) Traceback (most recent call last): ... AssertionError : The length of the inputs and expected_outputs must be equal Parameters: Name Type Description Default inputs Iterable [ Any ] The inputs. (X in scikit-learn) required expected_outputs Iterable [ T ] The ground-truth values corresponding to the inputs. (y in scikit-learn) required tags Union [ List [ str ], str ] A single tag or a list of tags to append to each generated trace's tags. [] train_split_ratio float The probability-weight of giving each trace the train tag. 1 test_split_ratio float The probability-weight of giving each trace the test tag. 0 validation_split_ratio float The probability-weight of giving each trace the validation tag. 0 Source code in great_ai/tracing/add_ground_truth.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 def add_ground_truth ( inputs : Iterable [ Any ], expected_outputs : Iterable [ T ], * , tags : Union [ List [ str ], str ] = [], train_split_ratio : float = 1 , test_split_ratio : float = 0 , validation_split_ratio : float = 0 ) -> None : \"\"\"Add training data (with optional train-test splitting). Add and tag data-points, wrap them into traces. The `inputs` are available via the `.input` property, while `expected_outputs` under both the `.output` and `.feedback` properties. All generated traces are tagged with `ground_truth` by default. Additional tags can be also provided. Using the `split_ratio` arguments, tags can be given randomly with a user-defined distribution. Only the ratio of the splits matter, they don't have to add up to 1. Examples: >>> add_ground_truth( ... [1, 2, 3], ... ['odd', 'even', 'odd'], ... tags='my_tag', ... train_split_ratio=1, ... test_split_ratio=1, ... validation_split_ratio=0.5, ... ) >>> add_ground_truth( ... [1, 2], ... ['odd', 'even', 'odd'], ... tags='my_tag', ... train_split_ratio=1, ... test_split_ratio=1, ... validation_split_ratio=0.5, ... ) Traceback (most recent call last): ... AssertionError: The length of the inputs and expected_outputs must be equal Args: inputs: The inputs. (X in scikit-learn) expected_outputs: The ground-truth values corresponding to the inputs. (y in scikit-learn) tags: A single tag or a list of tags to append to each generated trace's tags. train_split_ratio: The probability-weight of giving each trace the `train` tag. test_split_ratio: The probability-weight of giving each trace the `test` tag. validation_split_ratio: The probability-weight of giving each trace the `validation` tag. \"\"\" inputs = list ( inputs ) expected_outputs = list ( expected_outputs ) assert len ( inputs ) == len ( expected_outputs ), \"The length of the inputs and expected_outputs must be equal\" tags = tags if isinstance ( tags , list ) else [ tags ] sum_ratio = train_split_ratio + test_split_ratio + validation_split_ratio assert sum_ratio > 0 , \"The sum of the split ratios must be a positive number\" train_split_ratio /= sum_ratio test_split_ratio /= sum_ratio validation_split_ratio /= sum_ratio values = list ( zip ( inputs , expected_outputs )) shuffle ( values ) split_tags = ( [ TRAIN_SPLIT_TAG_NAME ] * ceil ( train_split_ratio * len ( inputs )) + [ TEST_SPLIT_TAG_NAME ] * ceil ( test_split_ratio * len ( inputs )) + [ VALIDATION_SPLIT_TAG_NAME ] * ceil ( validation_split_ratio * len ( inputs )) ) shuffle ( split_tags ) created = datetime . utcnow () . isoformat () traces = [ cast ( Trace [ T ], Trace ( # avoid ValueError: \"Trace\" object has no field \"__orig_class__\" trace_id = str ( uuid4 ()), created = created , original_execution_time_ms = 0 , logged_values = X if isinstance ( X , dict ) else { \"input\" : X }, models = [], output = y , feedback = y , exception = None , tags = [ GROUND_TRUTH_TAG_NAME , split_tag , * tags ], ), ) for (( X , y ), split_tag ) in zip ( values , split_tags ) ] get_context () . tracing_database . save_batch ( traces ) query_ground_truth ( conjunctive_tags = [], * , since = None , until = None , return_max_count = None ) # Return training samples. Combines, filters, and returns data-points that have been either added by add_ground_truth or were the result of a prediction after which their trace got a feedback through the RESP API-s /traces/{trace_id}/feedback endpoint (end-to-end feedback). Filtering can be used to only return points matching all given tags (or the single given tag) and by time of creation. Examples: >>> query_ground_truth () [...] Parameters: Name Type Description Default conjunctive_tags Union [ List [ str ], str ] Single tag or a list of tags which the returned traces have to match. The relationship between the tags is conjunctive (AND). [] since Optional [ datetime ] Only return traces created after the given timestamp. None means no filtering. None until Optional [ datetime ] Only return traces created before the given timestamp. None means no filtering. None return_max_count Optional [ int ] Return at-most this many traces. (take, limit) None Source code in great_ai/tracing/query_ground_truth.py 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 def query_ground_truth ( conjunctive_tags : Union [ List [ str ], str ] = [], * , since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , return_max_count : Optional [ int ] = None ) -> List [ Trace ]: \"\"\"Return training samples. Combines, filters, and returns data-points that have been either added by `add_ground_truth` or were the result of a prediction after which their trace got a feedback through the RESP API-s `/traces/{trace_id}/feedback` endpoint (end-to-end feedback). Filtering can be used to only return points matching all given tags (or the single given tag) and by time of creation. Examples: >>> query_ground_truth() [...] Args: conjunctive_tags: Single tag or a list of tags which the returned traces have to match. The relationship between the tags is conjunctive (AND). since: Only return traces created after the given timestamp. `None` means no filtering. until: Only return traces created before the given timestamp. `None` means no filtering. return_max_count: Return at-most this many traces. (take, limit) \"\"\" tags = ( conjunctive_tags if isinstance ( conjunctive_tags , list ) else [ conjunctive_tags ] ) db = get_context () . tracing_database items , length = db . query ( conjunctive_tags = tags , since = since , until = until , take = return_max_count , has_feedback = True , ) return items delete_ground_truth ( conjunctive_tags = [], * , since = None , until = None ) # Delete traces matching the given criteria. Takes the same arguments as query_ground_truth but instead of returning them, it simply deletes them. You can rely on the efficiency of the delete's implementation. Examples: >>> delete_ground_truth ([ 'train' , 'test' , 'validation' ]) Parameters: Name Type Description Default conjunctive_tags Union [ List [ str ], str ] Single tag or a list of tags which the deleted traces have to match. The relationship between the tags is conjunctive (AND). [] since Optional [ datetime ] Only delete traces created after the given timestamp. None means no filtering. None until Optional [ datetime ] Only delete traces created before the given timestamp. None means no filtering. None Source code in great_ai/tracing/delete_ground_truth.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 def delete_ground_truth ( conjunctive_tags : Union [ List [ str ], str ] = [], * , since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , ) -> None : \"\"\"Delete traces matching the given criteria. Takes the same arguments as `query_ground_truth` but instead of returning them, it simply deletes them. You can rely on the efficiency of the delete's implementation. Examples: >>> delete_ground_truth(['train', 'test', 'validation']) Args: conjunctive_tags: Single tag or a list of tags which the deleted traces have to match. The relationship between the tags is conjunctive (AND). since: Only delete traces created after the given timestamp. `None` means no filtering. until: Only delete traces created before the given timestamp. `None` means no filtering. \"\"\" tags = ( conjunctive_tags if isinstance ( conjunctive_tags , list ) else [ conjunctive_tags ] ) db = get_context () . tracing_database items , length = db . query ( conjunctive_tags = tags , until = until , since = since , has_feedback = True ) db . delete_batch ([ i . trace_id for i in items ]) Tracing databases # TracingDatabaseDriver # Bases: ABC Interface expected from a database to be used for storing traces. Source code in great_ai/persistence/tracing_database_driver.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 class TracingDatabaseDriver ( ABC ): \"\"\"Interface expected from a database to be used for storing traces.\"\"\" is_production_ready : bool initialized : bool = False @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()}) @classmethod def configure_credentials ( cls , ) -> None : cls . initialized = True @abstractmethod def save ( self , document : Trace ) -> str : pass @abstractmethod def save_batch ( self , documents : List [ Trace ], ) -> List [ str ]: pass @abstractmethod def get ( self , id : str ) -> Optional [ Trace ]: pass @abstractmethod def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], until : Optional [ datetime ] = None , since : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [] ) -> Tuple [ List [ Trace ], int ]: pass @abstractmethod def update ( self , id : str , new_version : Trace ) -> None : pass @abstractmethod def delete ( self , id : str ) -> None : pass @abstractmethod def delete_batch ( self , ids : List [ str ], ) -> None : pass MongoDbDriver # Bases: TracingDatabaseDriver TracingDatabaseDriver implementation using MongoDB as a backend. A production-ready database driver suitable for efficiently handling semi-structured data. Checkout MongoDB Atlas for a hosted MongoDB solution. Source code in great_ai/persistence/mongodb_driver.py 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 class MongoDbDriver ( TracingDatabaseDriver ): \"\"\"TracingDatabaseDriver implementation using MongoDB as a backend. A production-ready database driver suitable for efficiently handling semi-structured data. Checkout [MongoDB Atlas](https://www.mongodb.com/cloud/atlas/register) for a hosted MongoDB solution. \"\"\" is_production_ready = True mongo_connection_string : str mongo_database : str def __init__ ( self ) -> None : super () . __init__ () if self . mongo_connection_string is None or self . mongo_database is None : raise ValueError ( \"Please configure the MongoDB access options by calling \" \"MongoDbDriver.configure_credentials\" ) with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . create_index ( [( \"tags\" , ASCENDING ), ( \"created\" , DESCENDING )], background = True ) @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : \"\"\"Configure the connection details for MongoDB. Args: mongo_connection_string: For example: 'mongodb://my_user:my_pass@localhost:27017' mongo_database: Name of the database to use. If doesn't exist, it is created and initialised. \"\"\" cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () def save ( self , trace : Trace ) -> str : serialized = trace . to_flat_dict () serialized [ \"_id\" ] = trace . trace_id with MongoClient [ Any ]( self . mongo_connection_string ) as client : return client [ self . mongo_database ] . traces . insert_one ( serialized ) . inserted_id def save_batch ( self , documents : List [ Trace ]) -> List [ str ]: serialized = [ d . to_flat_dict () for d in documents ] for s in serialized : s [ \"_id\" ] = s [ \"trace_id\" ] with MongoClient [ Any ]( self . mongo_connection_string ) as client : return ( client [ self . mongo_database ] . traces . insert_many ( serialized , ordered = False ) . inserted_ids ) def get ( self , id : str ) -> Optional [ Trace ]: with MongoClient [ Any ]( self . mongo_connection_string ) as client : value = client [ self . mongo_database ] . traces . find_one ( id ) if value : value = Trace . parse_obj ( value ) return value def _get_operator ( self , filter : Filter ) -> str : if filter . operator == \"contains\" and not isinstance ( filter . value , str ): return operator_mapping [ \"=\" ] return operator_mapping [ filter . operator ] def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [], ) -> Tuple [ List [ Trace ], int ]: query : Dict [ str , Any ] = { \"filter\" : {}, } and_query : List [ Dict [ str , Any ]] = [] and_query . extend ({ \"tags\" : tag } for tag in conjunctive_tags ) and_query . extend ( { f . property : { self . _get_operator ( f ): f . value }} for f in conjunctive_filters ) if not and_query : and_query . append ({}) if since : and_query . append ({ \"created\" : { \"$gte\" : since }}) if until : and_query . append ({ \"created\" : { \"$lte\" : until }}) if has_feedback is not None : and_query . append ( { \"feedback\" : { \"$ne\" : None }} if has_feedback else { \"feedback\" : None } ) query [ \"filter\" ][ \"$and\" ] = and_query with MongoClient [ Any ]( self . mongo_connection_string ) as client : count = client [ self . mongo_database ] . traces . count_documents ( ** query ) if skip : query [ \"skip\" ] = skip if take : query [ \"limit\" ] = take query [ \"sort\" ] = [ ( col . column_id , 1 if col . direction == \"asc\" else - 1 ) for col in sort_by ] with client [ self . mongo_database ] . traces . find ( ** query ) as cursor : documents = [ Trace [ Any ] . parse_obj ( t ) for t in cursor ] return documents , count def update ( self , id : str , new_version : Trace ) -> None : serialized = new_version . to_flat_dict () serialized [ \"_id\" ] = new_version . trace_id with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . update_one ({ \"_id\" : id }, serialized ) def delete ( self , id : str ) -> None : with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . delete_one ({ \"_id\" : id }) def delete_batch ( self , ids : List [ str ]) -> None : with MongoClient [ Any ]( self . mongo_connection_string ) as client : for c in chunk ( ids , chunk_size = 10000 ): # avoid: 'delete' command document too large delete_filter = { \"_id\" : { \"$in\" : c }} client [ self . mongo_database ] . traces . delete_many ( delete_filter ) configure_credentials ( * , mongo_connection_string , mongo_database , ** _ ) classmethod # Configure the connection details for MongoDB. Parameters: Name Type Description Default mongo_connection_string str For example: 'mongodb://my_user:my_pass@localhost:27017' required mongo_database str Name of the database to use. If doesn't exist, it is created and initialised. required Source code in great_ai/persistence/mongodb_driver.py 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : \"\"\"Configure the connection details for MongoDB. Args: mongo_connection_string: For example: 'mongodb://my_user:my_pass@localhost:27017' mongo_database: Name of the database to use. If doesn't exist, it is created and initialised. \"\"\" cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () ParallelTinyDbDriver # Bases: TracingDatabaseDriver TracingDatabaseDriver with TinyDB as a backend. Saves the database as a JSON into a single file. Highly inefficient on inserting, not advised for production use. A multiprocessing lock protects the database file to avoid parallelisation issues. Source code in great_ai/persistence/parallel_tinydb_driver.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 class ParallelTinyDbDriver ( TracingDatabaseDriver ): \"\"\"TracingDatabaseDriver with TinyDB as a backend. Saves the database as a JSON into a single file. Highly inefficient on inserting, not advised for production use. A multiprocessing lock protects the database file to avoid parallelisation issues. \"\"\" is_production_ready = False path_to_db = Path ( DEFAULT_TRACING_DB_FILENAME ) def save ( self , trace : Trace ) -> str : return self . _safe_execute ( lambda db : db . insert ( trace . dict ())) def save_batch ( self , documents : List [ Trace ]) -> List [ str ]: traces = [ d . dict () for d in documents ] return self . _safe_execute ( lambda db : db . insert_multiple ( traces )) def get ( self , id : str ) -> Optional [ Trace ]: value = self . _safe_execute ( lambda db : db . get ( lambda d : d [ \"trace_id\" ] == id )) if value : value = Trace . parse_obj ( value ) return value def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [] ) -> Tuple [ List [ Trace ], int ]: def does_match ( d : Dict [ str , Any ]) -> bool : return ( not set ( conjunctive_tags ) - set ( d [ \"tags\" ]) and ( since is None or datetime . fromisoformat ( d [ \"created\" ]) >= since ) and ( until is None or datetime . fromisoformat ( d [ \"created\" ]) <= until ) and ( has_feedback is None or has_feedback == ( d [ \"feedback\" ] is not None ) ) ) documents : List [ Trace ] = [ Trace . parse_obj ( t ) for t in self . _safe_execute ( lambda db : db . search ( does_match )) ] if not documents : return [], 0 df = pd . DataFrame ([ d . to_flat_dict () for d in documents ]) for f in conjunctive_filters : operator = f . operator . lower () if operator in operator_mapping : df = df . loc [ getattr ( df [ f . property ], operator_mapping [ f . operator ])( f . value ) ] elif operator == \"contains\" : df = df . loc [ df [ f . property ] . str . contains ( f . value , case = False )] if sort_by : df . sort_values ( [ col . column_id for col in sort_by ], ascending = [ col . direction == \"asc\" for col in sort_by ], inplace = True , ) count = len ( df ) result = df . iloc [ skip :] if take is None else df . iloc [ skip : skip + take ] return [ next ( d for d in documents if d . trace_id == trace_id ) for trace_id in result [ \"trace_id\" ] ], count def update ( self , id : str , new_version : Trace ) -> None : self . _safe_execute ( lambda db : db . update ( new_version . dict (), lambda d : d [ \"trace_id\" ] == id ) ) def delete ( self , id : str ) -> None : self . _safe_execute ( lambda db : db . remove ( lambda d : d [ \"trace_id\" ] == id )) def delete_batch ( self , ids : List [ str ]) -> None : for i in ids : self . delete ( i ) def _safe_execute ( self , func : Callable [[ TinyDB ], Any ]) -> Any : with lock : with TinyDB ( self . path_to_db ) as db : return func ( db )","title":"GreatAI reference"},{"location":"reference/#greatai-reference","text":"from great_ai import *","title":"GreatAI reference"},{"location":"reference/#core","text":"","title":"Core"},{"location":"reference/#great_ai.GreatAI","text":"Bases: Generic [ T , V ] Wrapper for a prediction function providing the implementation of SE4ML best-practices. Provides caching (with argument freezing), a TracingContext during execution, the scaffolding of HTTP endpoints using FastAPI and a dashboard using Dash. Supports wrapping async and synchronous functions while also maintaining correct typing. Attributes: Name Type Description app FastAPI instance wrapping the scaffolded endpoints and the Dash app. version SemVer derived from the app's version and the model names and versions registered through use_model. Source code in great_ai/deploy/great_ai.py 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 class GreatAI ( Generic [ T , V ]): \"\"\"Wrapper for a prediction function providing the implementation of SE4ML best-practices. Provides caching (with argument freezing), a TracingContext during execution, the scaffolding of HTTP endpoints using FastAPI and a dashboard using Dash. Supports wrapping async and synchronous functions while also maintaining correct typing. Attributes: app: FastAPI instance wrapping the scaffolded endpoints and the Dash app. version: SemVer derived from the app's version and the model names and versions registered through use_model. \"\"\" __name__ : str # help for MyPy __doc__ : str # help for MyPy def __init__ ( self , func : Callable [ ... , Union [ V , Awaitable [ V ]]], ): \"\"\"Do not call this function directly, use GreatAI.create instead.\"\"\" func = automatically_decorate_parameters ( func ) get_function_metadata_store ( func ) . is_finalised = True self . _cached_func = self . _get_cached_traced_function ( func ) self . _wrapped_func = wraps ( func )( freeze_arguments ( self . _cached_func )) wraps ( func )( self ) self . __doc__ = ( f \"GreatAI wrapper for interacting with the ` { self . __name__ } ` \" + f \"function. \\n\\n { dedent ( self . __doc__ or '' ) } \" ) self . version = str ( get_context () . version ) flat_model_versions = \".\" . join ( f \" { k } -v { v } \" for k , v in model_versions ) if flat_model_versions : self . version += f \"+ { flat_model_versions } \" self . app = FastAPI ( title = snake_case_to_text ( self . __name__ ), version = self . version , description = self . __doc__ + f \" \\n\\n Find out more in the [dashboard]( { DASHBOARD_PATH } ).\" , docs_url = None , redoc_url = None , ) self . _bootstrap_rest_api () @overload @staticmethod def create ( # type: ignore # Overloaded function signatures 1 and 2 overlap with incompatible return types # https://github.com/python/mypy/issues/12759 func : Callable [ ... , Awaitable [ V ]], ) -> \"GreatAI[Awaitable[Trace[V]], V]\" : ... @overload @staticmethod def create ( func : Callable [ ... , V ], ) -> \"GreatAI[Trace[V], V]\" : ... @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a [@parameter][great_ai.parameter] or [@use_model][great_ai.use_model] decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see [great_ai.configure][]. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , ) def __call__ ( self , * args : Any , ** kwargs : Any ) -> T : return self . _wrapped_func ( * args , ** kwargs ) @overload def process_batch ( self , batch : Sequence [ Tuple ], * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ True ], do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... @overload def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ False ] = ... , do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : bool = False , do_not_persist_traces : bool = False , ) -> List [ Trace [ V ]]: \"\"\"Map the wrapped function over a list of input_values (`batch`). A wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] providing type-safety and a progressbar through tqdm. Args: batch: A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set `unpack_arguments=True`. concurrency: Number of processes to start. Don't set it too much higher than the number of available CPU cores. unpack_arguments: Expect a list of tuples and unpack the tuples before giving them to the wrapped function. do_not_persist_traces: Don't save the traces in the database. Useful for evaluations run part of the CI. \"\"\" wrapped_function = self . _wrapped_func def inner ( value : Any ) -> T : return ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ) async def inner_async ( value : Any ) -> T : return await cast ( Awaitable , ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ), ) return list ( tqdm ( parallel_map ( inner_async if get_function_metadata_store ( self ) . is_asynchronous else inner , batch , concurrency = concurrency , ), total = len ( batch ), ) ) @staticmethod def _get_cached_traced_function ( func : Callable [ ... , Union [ V , Awaitable [ V ]]] ) -> Callable [ ... , T ]: @lru_cache ( maxsize = get_context () . prediction_cache_size ) def func_in_tracing_context_sync ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = func ( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) @alru_cache ( maxsize = get_context () . prediction_cache_size ) async def func_in_tracing_context_async ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = await cast ( Callable [ ... , Awaitable ], func )( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) return cast ( Callable [ ... , T ], ( func_in_tracing_context_async if get_function_metadata_store ( func ) . is_asynchronous else func_in_tracing_context_sync ), ) def _bootstrap_rest_api ( self , ) -> None : route_config = get_context () . route_config if route_config . prediction_endpoint_enabled : bootstrap_prediction_endpoint ( self . app , self . _wrapped_func ) if route_config . docs_endpoints_enabled : bootstrap_docs_endpoints ( self . app ) if route_config . dashboard_enabled : bootstrap_dashboard ( self . app , function_name = self . __name__ , documentation = self . __doc__ , ) if route_config . trace_endpoints_enabled : bootstrap_trace_endpoints ( self . app ) if route_config . feedback_endpoints_enabled : bootstrap_feedback_endpoints ( self . app ) if route_config . meta_endpoints_enabled : bootstrap_meta_endpoints ( self . app , self . _cached_func , ApiMetadata ( name = self . __name__ , version = self . version , documentation = self . __doc__ , configuration = get_context () . to_flat_dict (), ), )","title":"GreatAI"},{"location":"reference/#great_ai.deploy.great_ai.GreatAI.create","text":"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a @parameter or @use_model decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the .output property. For configuration options, see great_ai.configure . Examples: >>> @GreatAI . create ... def my_function ( a ): ... return a + 2 >>> my_function ( 3 ) . output 5 >>> @GreatAI . create ... def my_function ( a : int ) -> int : ... return a + 2 >>> my_function ( 3 ) Trace[int]... >>> my_function ( '3' ) . output Traceback (most recent call last): ... TypeError : type of a must be int; got str instead Parameters: Name Type Description Default func Union [ Callable [..., Awaitable [ V ]], Callable [..., V ]] The prediction function that needs to be decorated. required Returns: Type Description Union [ GreatAI[Awaitable[Trace[V]], V] , GreatAI[Trace[V], V] ] A GreatAI instance wrapping func . Source code in great_ai/deploy/great_ai.py 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a [@parameter][great_ai.parameter] or [@use_model][great_ai.use_model] decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see [great_ai.configure][]. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , )","title":"create()"},{"location":"reference/#great_ai.deploy.great_ai.GreatAI.process_batch","text":"Map the wrapped function over a list of input_values ( batch ). A wrapper over parallel_map providing type-safety and a progressbar through tqdm. Parameters: Name Type Description Default batch Sequence A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set unpack_arguments=True . required concurrency Optional [ int ] Number of processes to start. Don't set it too much higher than the number of available CPU cores. None unpack_arguments bool Expect a list of tuples and unpack the tuples before giving them to the wrapped function. False do_not_persist_traces bool Don't save the traces in the database. Useful for evaluations run part of the CI. False Source code in great_ai/deploy/great_ai.py 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : bool = False , do_not_persist_traces : bool = False , ) -> List [ Trace [ V ]]: \"\"\"Map the wrapped function over a list of input_values (`batch`). A wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] providing type-safety and a progressbar through tqdm. Args: batch: A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set `unpack_arguments=True`. concurrency: Number of processes to start. Don't set it too much higher than the number of available CPU cores. unpack_arguments: Expect a list of tuples and unpack the tuples before giving them to the wrapped function. do_not_persist_traces: Don't save the traces in the database. Useful for evaluations run part of the CI. \"\"\" wrapped_function = self . _wrapped_func def inner ( value : Any ) -> T : return ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ) async def inner_async ( value : Any ) -> T : return await cast ( Awaitable , ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ), ) return list ( tqdm ( parallel_map ( inner_async if get_function_metadata_store ( self ) . is_asynchronous else inner , batch , concurrency = concurrency , ), total = len ( batch ), ) )","title":"process_batch()"},{"location":"reference/#great_ai.configure","text":"Set the global configuration used by the great-ai library. You must call configure before calling (or decorating with) any other great-ai function. If tracing_database_factory or large_file_implementation is not specified, their default value is determined based on which TracingDatabase and LargeFile has been configured (e.g.: LargeFileS3.configure_credentials_from_file('s3.ini')), or whether there is any file named s3.ini or mongo.ini in the working directory. Examples: >>> configure ( prediction_cache_size = 0 ) Parameters: Name Type Description Default version Union [ int , str ] The version of your application (using SemVer is recommended). '0.0.1' log_level int Set the default logging level of logging . DEBUG seed int Set seed of random (and numpy if installed) for reproducibility. 42 tracing_database_factory Optional [ Type [ TracingDatabaseDriver ]] Specify a different TracingDatabaseDriver than the one already configured. None large_file_implementation Optional [ Type [ LargeFileBase ]] Specify a different LargeFile than the one already configured. None should_log_exception_stack Optional [ bool ] Log the traces of unhandled exceptions. None prediction_cache_size int Size of the LRU cache applied over the prediction functions. 512 disable_se4ml_banner bool Turn off the warning about the importance of SE4ML best- practices. False dashboard_table_size int Number of rows to display in the dashboard's table. 50 route_config RouteConfig Enable or disable specific HTTP API endpoints. RouteConfig() Source code in great_ai/context.py 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 def configure ( * , version : Union [ int , str ] = \"0.0.1\" , log_level : int = DEBUG , seed : int = 42 , tracing_database_factory : Optional [ Type [ TracingDatabaseDriver ]] = None , large_file_implementation : Optional [ Type [ LargeFileBase ]] = None , should_log_exception_stack : Optional [ bool ] = None , prediction_cache_size : int = 512 , disable_se4ml_banner : bool = False , dashboard_table_size : int = 50 , route_config : RouteConfig = RouteConfig (), ) -> None : \"\"\"Set the global configuration used by the great-ai library. You must call `configure` before calling (or decorating with) any other great-ai function. If `tracing_database_factory` or `large_file_implementation` is not specified, their default value is determined based on which TracingDatabase and LargeFile has been configured (e.g.: LargeFileS3.configure_credentials_from_file('s3.ini')), or whether there is any file named s3.ini or mongo.ini in the working directory. Examples: >>> configure(prediction_cache_size=0) Arguments: version: The version of your application (using SemVer is recommended). log_level: Set the default logging level of `logging`. seed: Set seed of `random` (and `numpy` if installed) for reproducibility. tracing_database_factory: Specify a different TracingDatabaseDriver than the one already configured. large_file_implementation: Specify a different LargeFile than the one already configured. should_log_exception_stack: Log the traces of unhandled exceptions. prediction_cache_size: Size of the LRU cache applied over the prediction functions. disable_se4ml_banner: Turn off the warning about the importance of SE4ML best- practices. dashboard_table_size: Number of rows to display in the dashboard's table. route_config: Enable or disable specific HTTP API endpoints. \"\"\" global _context logger = get_logger ( \"great_ai\" , level = log_level ) if _context is not None : logger . error ( \"Configuration has been already initialised, overwriting. \\n \" + \"Make sure to call `configure()` before importing your application code.\" ) is_production = _is_in_production_mode ( logger = logger ) _set_seed ( seed ) tracing_database_factory = _initialize_tracing_database ( tracing_database_factory , logger = logger ) tracing_database = tracing_database_factory () if not tracing_database . is_production_ready : message = f \"\"\"The selected tracing database ( { tracing_database_factory . __name__ } ) is not recommended for production\"\"\" if is_production : logger . error ( message ) else : logger . warning ( message ) _context = Context ( version = version , tracing_database = tracing_database , large_file_implementation = _initialize_large_file ( large_file_implementation , logger = logger ), is_production = is_production , logger = logger , should_log_exception_stack = not is_production if should_log_exception_stack is None else should_log_exception_stack , prediction_cache_size = prediction_cache_size , dashboard_table_size = dashboard_table_size , route_config = route_config , ) logger . info ( f \"GreatAI (v { __version__ } ): configured \u2705\" ) for k , v in get_context () . to_flat_dict () . items (): logger . info ( f \" { LIST_ITEM_PREFIX }{ k } : { v } \" ) if not is_production and not disable_se4ml_banner : logger . warning ( \"You still need to check whether you follow all best practices before \" \"trusting your deployment.\" ) logger . warning ( f \"> Find out more at { SE4ML_WEBSITE } \" )","title":"configure()"},{"location":"reference/#great_ai.save_model","text":"Save (and optionally serialise) a model in order to use by use_model . The model can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case model is an object, it is serialised using dill before uploading it. Examples: >>> from great_ai import use_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Parameters: Name Type Description Default model Union [ Path , str , object ] The object or path to be uploaded. required key str The model's name. required keep_last_n Optional [ int ] If specified, remove old models and only keep the latest n. Directly passed to LargeFile. None Returns: Type Description str The key and version of the saved model separated by a colon. Example: \"key:version\" Source code in great_ai/models/save_model.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 def save_model ( model : Union [ Path , str , object ], key : str , * , keep_last_n : Optional [ int ] = None ) -> str : \"\"\"Save (and optionally serialise) a model in order to use by `use_model`. The `model` can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case `model` is an object, it is serialised using `dill` before uploading it. Examples: >>> from great_ai import use_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: model: The object or path to be uploaded. key: The model's name. keep_last_n: If specified, remove old models and only keep the latest n. Directly passed to LargeFile. Returns: The key and version of the saved model separated by a colon. Example: \"key:version\" \"\"\" file = get_context () . large_file_implementation ( name = key , mode = \"wb\" , keep_last_n = keep_last_n ) if isinstance ( model , Path ) or isinstance ( model , str ): file . push ( model ) else : with file as f : dump ( model , f ) get_context () . logger . info ( f \"Model { key } uploaded with version { file . version } \" ) return f \" { key } : { file . version } \"","title":"save_model()"},{"location":"reference/#great_ai.use_model","text":"Inject a model into a function. Load a model specified by key and version using the currently active LargeFile implementation. If it's a single object, it is deserialised using dill . If it's a directory of files, a pathlib.Path instance is given. By default, the function's model parameter is replaced by the loaded model. This can be customised by changing model_kwarg_name . Multiple models can be loaded by decorating the same function with use_model multiple times. Examples: >>> from great_ai import save_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Args: key: The model's name as stored by the LargeFile implementation. version: The model's version as stored by the LargeFile implementation. model_kwarg_name: the parameter to use for injecting the loaded model Returns: A decorator for model injection. Source code in great_ai/models/use_model.py 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 def use_model ( key : str , * , version : Union [ int , Literal [ \"latest\" ]] = \"latest\" , model_kwarg_name : str = \"model\" , ) -> Callable [[ F ], F ]: \"\"\"Inject a model into a function. Load a model specified by `key` and `version` using the currently active `LargeFile` implementation. If it's a single object, it is deserialised using `dill`. If it's a directory of files, a `pathlib.Path` instance is given. By default, the function's `model` parameter is replaced by the loaded model. This can be customised by changing `model_kwarg_name`. Multiple models can be loaded by decorating the same function with `use_model` multiple times. Examples: >>> from great_ai import save_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: key: The model's name as stored by the LargeFile implementation. version: The model's version as stored by the LargeFile implementation. model_kwarg_name: the parameter to use for injecting the loaded model Returns: A decorator for model injection. \"\"\" assert ( isinstance ( version , int ) or version == \"latest\" ), \"Only integers or the string literal `latest` is allowed as a version\" model , actual_version = _load_model ( key = key , version = None if version == \"latest\" else version , ) def decorator ( func : F ) -> F : assert_function_is_not_finalised ( func ) store = get_function_metadata_store ( func ) store . model_parameter_names . append ( model_kwarg_name ) @wraps ( func ) def wrapper ( * args : List [ Any ], ** kwargs : Dict [ str , Any ]) -> Any : tracing_context = TracingContext . get_current_tracing_context () if tracing_context : tracing_context . log_model ( Model ( key = key , version = actual_version )) return func ( * args , ** kwargs , ** { model_kwarg_name : model }) return cast ( F , wrapper ) return decorator","title":"use_model()"},{"location":"reference/#great_ai.parameter","text":"Control the validation and logging of function parameters. Examples: >>> @parameter ( 'a' ) ... def my_function ( a : int ): ... return a + 2 >>> my_function ( 4 ) 6 >>> my_function ( '3' ) Traceback (most recent call last): ... TypeError : type of a must be int; got str instead >>> @parameter ( 'positive_a' , validate = lambda v : v > 0 ) ... def my_function ( positive_a : int ): ... return a + 2 >>> my_function ( - 1 ) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError : ... Parameters: Name Type Description Default parameter_name str Name of parameter to consider required validate Callable [[ Any ], bool ] Optional validate to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. lambda _: True disable_logging bool Do not save the value in any active TracingContext. False Returns: Type Description Callable [[ F ], F ] A decorator for argument validation. Source code in great_ai/parameters/parameter.py 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 def parameter ( parameter_name : str , * , validate : Callable [[ Any ], bool ] = lambda _ : True , disable_logging : bool = False , ) -> Callable [[ F ], F ]: \"\"\"Control the validation and logging of function parameters. Examples: >>> @parameter('a') ... def my_function(a: int): ... return a + 2 >>> my_function(4) 6 >>> my_function('3') Traceback (most recent call last): ... TypeError: type of a must be int; got str instead >>> @parameter('positive_a', validate=lambda v: v > 0) ... def my_function(positive_a: int): ... return a + 2 >>> my_function(-1) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError: ... Args: parameter_name: Name of parameter to consider validate: Optional validate to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. disable_logging: Do not save the value in any active TracingContext. Returns: A decorator for argument validation. \"\"\" def decorator ( func : F ) -> F : get_function_metadata_store ( func ) . input_parameter_names . append ( parameter_name ) assert_function_is_not_finalised ( func ) actual_name = f \"arg: { parameter_name } \" @wraps ( func ) def wrapper ( * args : Any , ** kwargs : Dict [ str , Any ]) -> Any : arguments = get_arguments ( func , args , kwargs ) argument = arguments . get ( parameter_name ) expected_type = func . __annotations__ . get ( parameter_name ) if expected_type is not None : check_type ( parameter_name , argument , expected_type ) if not validate ( argument ): raise ArgumentValidationError ( f \"\"\"Argument { parameter_name } in { func . __name__ } did not pass validation\"\"\" ) context = TracingContext . get_current_tracing_context () if context and not disable_logging : context . log_value ( name = f \" { actual_name } :value\" , value = argument ) if isinstance ( argument , str ): context . log_value ( name = f \" { actual_name } :length\" , value = len ( argument )) return func ( * args , ** kwargs ) return cast ( F , wrapper ) return decorator","title":"parameter()"},{"location":"reference/#great_ai.log_metric","text":"Source code in great_ai/parameters/log_metric.py 8 9 10 11 12 13 14 15 def log_metric ( argument_name : str , value : Any ) -> None : tracing_context = TracingContext . get_current_tracing_context () caller = inspect . stack ()[ 1 ] . function actual_name = f \"metric: { caller } : { argument_name } \" if tracing_context : tracing_context . log_value ( name = actual_name , value = value ) get_context () . logger . info ( f \" { actual_name } = { value } \" )","title":"log_metric()"},{"location":"reference/#remote-calls","text":"","title":"Remote calls"},{"location":"reference/#great_ai.call_remote_great_ai","text":"Communicate with a GreatAI object through an HTTP request. Wrapper over call_remote_great_ai_async making it synchronous. For more info, see call_remote_great_ai_async . Source code in great_ai/remote/call_remote_great_ai.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 def call_remote_great_ai ( base_uri : str , data : Mapping [ str , Any ], retry_count : int = 4 , timeout_in_seconds : Optional [ int ] = 300 , model_class : Optional [ Type [ T ]] = None , ) -> Trace [ T ]: \"\"\"Communicate with a GreatAI object through an HTTP request. Wrapper over `call_remote_great_ai_async` making it synchronous. For more info, see `call_remote_great_ai_async`. \"\"\" try : asyncio . get_running_loop () raise Exception ( f \"Already running in an event loop, you have to call ` { call_remote_great_ai_async . __name__ } `\" ) except RuntimeError : pass future = call_remote_great_ai_async ( base_uri = base_uri , data = data , retry_count = retry_count , timeout_in_seconds = timeout_in_seconds , model_class = model_class , ) return asyncio . run ( future )","title":"call_remote_great_ai()"},{"location":"reference/#great_ai.call_remote_great_ai_async","text":"Communicate with a GreatAI object through an HTTP request. Send a POST request using httpx to implement a remote call. Error-handling and retries are provided by httpx. The return value is inflated into a Trace. If model_class is specified, the original output is deserialised. Parameters: Name Type Description Default base_uri str Address of the remote instance, example: 'http://localhost:6060' required data Mapping [ str , Any ] The input sent as a json to the remote instance. required retry_count int Retry on any HTTP communication failure. 4 timeout_in_seconds Optional [ int ] Overall permissable max length of the request. None means no timeout. 300 model_class Optional [ Type [ T ]] A subtype of BaseModel to be used for deserialising the .output of the trace. None Source code in great_ai/remote/call_remote_great_ai_async.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 async def call_remote_great_ai_async ( base_uri : str , data : Mapping [ str , Any ], retry_count : int = 4 , timeout_in_seconds : Optional [ int ] = 300 , model_class : Optional [ Type [ T ]] = None , ) -> Trace [ T ]: \"\"\"Communicate with a GreatAI object through an HTTP request. Send a POST request using [httpx](https://www.python-httpx.org/) to implement a remote call. Error-handling and retries are provided by httpx. The return value is inflated into a Trace. If `model_class` is specified, the original output is deserialised. Args: base_uri: Address of the remote instance, example: 'http://localhost:6060' data: The input sent as a json to the remote instance. retry_count: Retry on any HTTP communication failure. timeout_in_seconds: Overall permissable max length of the request. `None` means no timeout. model_class: A subtype of BaseModel to be used for deserialising the `.output` of the trace. \"\"\" if base_uri . endswith ( \"/\" ): base_uri = base_uri [: - 1 ] if not base_uri . endswith ( \"/predict\" ): base_uri = f \" { base_uri } /predict\" transport = httpx . AsyncHTTPTransport ( retries = retry_count ) try : async with httpx . AsyncClient ( transport = transport , timeout = timeout_in_seconds ) as client : response = await client . post ( base_uri , json = data ) try : response . raise_for_status () except Exception : raise RemoteCallError ( f \"Unexpected status code, reason: { response . text } \" ) except Exception as e : raise RemoteCallError from e try : trace = response . json () except Exception : raise RemoteCallError ( f \"JSON parsing failed { response . text } \" , ) try : if model_class is not None : trace [ \"output\" ] = model_class . parse_obj ( trace [ \"output\" ]) return Trace . parse_obj ( trace ) except Exception : raise RemoteCallError ( \"Could not parse Trace\" )","title":"call_remote_great_ai_async()"},{"location":"reference/#ground-truth","text":"","title":"Ground-truth"},{"location":"reference/#great_ai.add_ground_truth","text":"Add training data (with optional train-test splitting). Add and tag data-points, wrap them into traces. The inputs are available via the .input property, while expected_outputs under both the .output and .feedback properties. All generated traces are tagged with ground_truth by default. Additional tags can be also provided. Using the split_ratio arguments, tags can be given randomly with a user-defined distribution. Only the ratio of the splits matter, they don't have to add up to 1. Examples: >>> add_ground_truth ( ... [ 1 , 2 , 3 ], ... [ 'odd' , 'even' , 'odd' ], ... tags = 'my_tag' , ... train_split_ratio = 1 , ... test_split_ratio = 1 , ... validation_split_ratio = 0.5 , ... ) >>> add_ground_truth ( ... [ 1 , 2 ], ... [ 'odd' , 'even' , 'odd' ], ... tags = 'my_tag' , ... train_split_ratio = 1 , ... test_split_ratio = 1 , ... validation_split_ratio = 0.5 , ... ) Traceback (most recent call last): ... AssertionError : The length of the inputs and expected_outputs must be equal Parameters: Name Type Description Default inputs Iterable [ Any ] The inputs. (X in scikit-learn) required expected_outputs Iterable [ T ] The ground-truth values corresponding to the inputs. (y in scikit-learn) required tags Union [ List [ str ], str ] A single tag or a list of tags to append to each generated trace's tags. [] train_split_ratio float The probability-weight of giving each trace the train tag. 1 test_split_ratio float The probability-weight of giving each trace the test tag. 0 validation_split_ratio float The probability-weight of giving each trace the validation tag. 0 Source code in great_ai/tracing/add_ground_truth.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 def add_ground_truth ( inputs : Iterable [ Any ], expected_outputs : Iterable [ T ], * , tags : Union [ List [ str ], str ] = [], train_split_ratio : float = 1 , test_split_ratio : float = 0 , validation_split_ratio : float = 0 ) -> None : \"\"\"Add training data (with optional train-test splitting). Add and tag data-points, wrap them into traces. The `inputs` are available via the `.input` property, while `expected_outputs` under both the `.output` and `.feedback` properties. All generated traces are tagged with `ground_truth` by default. Additional tags can be also provided. Using the `split_ratio` arguments, tags can be given randomly with a user-defined distribution. Only the ratio of the splits matter, they don't have to add up to 1. Examples: >>> add_ground_truth( ... [1, 2, 3], ... ['odd', 'even', 'odd'], ... tags='my_tag', ... train_split_ratio=1, ... test_split_ratio=1, ... validation_split_ratio=0.5, ... ) >>> add_ground_truth( ... [1, 2], ... ['odd', 'even', 'odd'], ... tags='my_tag', ... train_split_ratio=1, ... test_split_ratio=1, ... validation_split_ratio=0.5, ... ) Traceback (most recent call last): ... AssertionError: The length of the inputs and expected_outputs must be equal Args: inputs: The inputs. (X in scikit-learn) expected_outputs: The ground-truth values corresponding to the inputs. (y in scikit-learn) tags: A single tag or a list of tags to append to each generated trace's tags. train_split_ratio: The probability-weight of giving each trace the `train` tag. test_split_ratio: The probability-weight of giving each trace the `test` tag. validation_split_ratio: The probability-weight of giving each trace the `validation` tag. \"\"\" inputs = list ( inputs ) expected_outputs = list ( expected_outputs ) assert len ( inputs ) == len ( expected_outputs ), \"The length of the inputs and expected_outputs must be equal\" tags = tags if isinstance ( tags , list ) else [ tags ] sum_ratio = train_split_ratio + test_split_ratio + validation_split_ratio assert sum_ratio > 0 , \"The sum of the split ratios must be a positive number\" train_split_ratio /= sum_ratio test_split_ratio /= sum_ratio validation_split_ratio /= sum_ratio values = list ( zip ( inputs , expected_outputs )) shuffle ( values ) split_tags = ( [ TRAIN_SPLIT_TAG_NAME ] * ceil ( train_split_ratio * len ( inputs )) + [ TEST_SPLIT_TAG_NAME ] * ceil ( test_split_ratio * len ( inputs )) + [ VALIDATION_SPLIT_TAG_NAME ] * ceil ( validation_split_ratio * len ( inputs )) ) shuffle ( split_tags ) created = datetime . utcnow () . isoformat () traces = [ cast ( Trace [ T ], Trace ( # avoid ValueError: \"Trace\" object has no field \"__orig_class__\" trace_id = str ( uuid4 ()), created = created , original_execution_time_ms = 0 , logged_values = X if isinstance ( X , dict ) else { \"input\" : X }, models = [], output = y , feedback = y , exception = None , tags = [ GROUND_TRUTH_TAG_NAME , split_tag , * tags ], ), ) for (( X , y ), split_tag ) in zip ( values , split_tags ) ] get_context () . tracing_database . save_batch ( traces )","title":"add_ground_truth()"},{"location":"reference/#great_ai.query_ground_truth","text":"Return training samples. Combines, filters, and returns data-points that have been either added by add_ground_truth or were the result of a prediction after which their trace got a feedback through the RESP API-s /traces/{trace_id}/feedback endpoint (end-to-end feedback). Filtering can be used to only return points matching all given tags (or the single given tag) and by time of creation. Examples: >>> query_ground_truth () [...] Parameters: Name Type Description Default conjunctive_tags Union [ List [ str ], str ] Single tag or a list of tags which the returned traces have to match. The relationship between the tags is conjunctive (AND). [] since Optional [ datetime ] Only return traces created after the given timestamp. None means no filtering. None until Optional [ datetime ] Only return traces created before the given timestamp. None means no filtering. None return_max_count Optional [ int ] Return at-most this many traces. (take, limit) None Source code in great_ai/tracing/query_ground_truth.py 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 def query_ground_truth ( conjunctive_tags : Union [ List [ str ], str ] = [], * , since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , return_max_count : Optional [ int ] = None ) -> List [ Trace ]: \"\"\"Return training samples. Combines, filters, and returns data-points that have been either added by `add_ground_truth` or were the result of a prediction after which their trace got a feedback through the RESP API-s `/traces/{trace_id}/feedback` endpoint (end-to-end feedback). Filtering can be used to only return points matching all given tags (or the single given tag) and by time of creation. Examples: >>> query_ground_truth() [...] Args: conjunctive_tags: Single tag or a list of tags which the returned traces have to match. The relationship between the tags is conjunctive (AND). since: Only return traces created after the given timestamp. `None` means no filtering. until: Only return traces created before the given timestamp. `None` means no filtering. return_max_count: Return at-most this many traces. (take, limit) \"\"\" tags = ( conjunctive_tags if isinstance ( conjunctive_tags , list ) else [ conjunctive_tags ] ) db = get_context () . tracing_database items , length = db . query ( conjunctive_tags = tags , since = since , until = until , take = return_max_count , has_feedback = True , ) return items","title":"query_ground_truth()"},{"location":"reference/#great_ai.delete_ground_truth","text":"Delete traces matching the given criteria. Takes the same arguments as query_ground_truth but instead of returning them, it simply deletes them. You can rely on the efficiency of the delete's implementation. Examples: >>> delete_ground_truth ([ 'train' , 'test' , 'validation' ]) Parameters: Name Type Description Default conjunctive_tags Union [ List [ str ], str ] Single tag or a list of tags which the deleted traces have to match. The relationship between the tags is conjunctive (AND). [] since Optional [ datetime ] Only delete traces created after the given timestamp. None means no filtering. None until Optional [ datetime ] Only delete traces created before the given timestamp. None means no filtering. None Source code in great_ai/tracing/delete_ground_truth.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 def delete_ground_truth ( conjunctive_tags : Union [ List [ str ], str ] = [], * , since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , ) -> None : \"\"\"Delete traces matching the given criteria. Takes the same arguments as `query_ground_truth` but instead of returning them, it simply deletes them. You can rely on the efficiency of the delete's implementation. Examples: >>> delete_ground_truth(['train', 'test', 'validation']) Args: conjunctive_tags: Single tag or a list of tags which the deleted traces have to match. The relationship between the tags is conjunctive (AND). since: Only delete traces created after the given timestamp. `None` means no filtering. until: Only delete traces created before the given timestamp. `None` means no filtering. \"\"\" tags = ( conjunctive_tags if isinstance ( conjunctive_tags , list ) else [ conjunctive_tags ] ) db = get_context () . tracing_database items , length = db . query ( conjunctive_tags = tags , until = until , since = since , has_feedback = True ) db . delete_batch ([ i . trace_id for i in items ])","title":"delete_ground_truth()"},{"location":"reference/#tracing-databases","text":"","title":"Tracing databases"},{"location":"reference/#great_ai.TracingDatabaseDriver","text":"Bases: ABC Interface expected from a database to be used for storing traces. Source code in great_ai/persistence/tracing_database_driver.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 class TracingDatabaseDriver ( ABC ): \"\"\"Interface expected from a database to be used for storing traces.\"\"\" is_production_ready : bool initialized : bool = False @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()}) @classmethod def configure_credentials ( cls , ) -> None : cls . initialized = True @abstractmethod def save ( self , document : Trace ) -> str : pass @abstractmethod def save_batch ( self , documents : List [ Trace ], ) -> List [ str ]: pass @abstractmethod def get ( self , id : str ) -> Optional [ Trace ]: pass @abstractmethod def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], until : Optional [ datetime ] = None , since : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [] ) -> Tuple [ List [ Trace ], int ]: pass @abstractmethod def update ( self , id : str , new_version : Trace ) -> None : pass @abstractmethod def delete ( self , id : str ) -> None : pass @abstractmethod def delete_batch ( self , ids : List [ str ], ) -> None : pass","title":"TracingDatabaseDriver"},{"location":"reference/#great_ai.MongoDbDriver","text":"Bases: TracingDatabaseDriver TracingDatabaseDriver implementation using MongoDB as a backend. A production-ready database driver suitable for efficiently handling semi-structured data. Checkout MongoDB Atlas for a hosted MongoDB solution. Source code in great_ai/persistence/mongodb_driver.py 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 class MongoDbDriver ( TracingDatabaseDriver ): \"\"\"TracingDatabaseDriver implementation using MongoDB as a backend. A production-ready database driver suitable for efficiently handling semi-structured data. Checkout [MongoDB Atlas](https://www.mongodb.com/cloud/atlas/register) for a hosted MongoDB solution. \"\"\" is_production_ready = True mongo_connection_string : str mongo_database : str def __init__ ( self ) -> None : super () . __init__ () if self . mongo_connection_string is None or self . mongo_database is None : raise ValueError ( \"Please configure the MongoDB access options by calling \" \"MongoDbDriver.configure_credentials\" ) with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . create_index ( [( \"tags\" , ASCENDING ), ( \"created\" , DESCENDING )], background = True ) @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : \"\"\"Configure the connection details for MongoDB. Args: mongo_connection_string: For example: 'mongodb://my_user:my_pass@localhost:27017' mongo_database: Name of the database to use. If doesn't exist, it is created and initialised. \"\"\" cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () def save ( self , trace : Trace ) -> str : serialized = trace . to_flat_dict () serialized [ \"_id\" ] = trace . trace_id with MongoClient [ Any ]( self . mongo_connection_string ) as client : return client [ self . mongo_database ] . traces . insert_one ( serialized ) . inserted_id def save_batch ( self , documents : List [ Trace ]) -> List [ str ]: serialized = [ d . to_flat_dict () for d in documents ] for s in serialized : s [ \"_id\" ] = s [ \"trace_id\" ] with MongoClient [ Any ]( self . mongo_connection_string ) as client : return ( client [ self . mongo_database ] . traces . insert_many ( serialized , ordered = False ) . inserted_ids ) def get ( self , id : str ) -> Optional [ Trace ]: with MongoClient [ Any ]( self . mongo_connection_string ) as client : value = client [ self . mongo_database ] . traces . find_one ( id ) if value : value = Trace . parse_obj ( value ) return value def _get_operator ( self , filter : Filter ) -> str : if filter . operator == \"contains\" and not isinstance ( filter . value , str ): return operator_mapping [ \"=\" ] return operator_mapping [ filter . operator ] def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [], ) -> Tuple [ List [ Trace ], int ]: query : Dict [ str , Any ] = { \"filter\" : {}, } and_query : List [ Dict [ str , Any ]] = [] and_query . extend ({ \"tags\" : tag } for tag in conjunctive_tags ) and_query . extend ( { f . property : { self . _get_operator ( f ): f . value }} for f in conjunctive_filters ) if not and_query : and_query . append ({}) if since : and_query . append ({ \"created\" : { \"$gte\" : since }}) if until : and_query . append ({ \"created\" : { \"$lte\" : until }}) if has_feedback is not None : and_query . append ( { \"feedback\" : { \"$ne\" : None }} if has_feedback else { \"feedback\" : None } ) query [ \"filter\" ][ \"$and\" ] = and_query with MongoClient [ Any ]( self . mongo_connection_string ) as client : count = client [ self . mongo_database ] . traces . count_documents ( ** query ) if skip : query [ \"skip\" ] = skip if take : query [ \"limit\" ] = take query [ \"sort\" ] = [ ( col . column_id , 1 if col . direction == \"asc\" else - 1 ) for col in sort_by ] with client [ self . mongo_database ] . traces . find ( ** query ) as cursor : documents = [ Trace [ Any ] . parse_obj ( t ) for t in cursor ] return documents , count def update ( self , id : str , new_version : Trace ) -> None : serialized = new_version . to_flat_dict () serialized [ \"_id\" ] = new_version . trace_id with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . update_one ({ \"_id\" : id }, serialized ) def delete ( self , id : str ) -> None : with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . delete_one ({ \"_id\" : id }) def delete_batch ( self , ids : List [ str ]) -> None : with MongoClient [ Any ]( self . mongo_connection_string ) as client : for c in chunk ( ids , chunk_size = 10000 ): # avoid: 'delete' command document too large delete_filter = { \"_id\" : { \"$in\" : c }} client [ self . mongo_database ] . traces . delete_many ( delete_filter )","title":"MongoDbDriver"},{"location":"reference/#great_ai.persistence.mongodb_driver.MongoDbDriver.configure_credentials","text":"Configure the connection details for MongoDB. Parameters: Name Type Description Default mongo_connection_string str For example: 'mongodb://my_user:my_pass@localhost:27017' required mongo_database str Name of the database to use. If doesn't exist, it is created and initialised. required Source code in great_ai/persistence/mongodb_driver.py 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : \"\"\"Configure the connection details for MongoDB. Args: mongo_connection_string: For example: 'mongodb://my_user:my_pass@localhost:27017' mongo_database: Name of the database to use. If doesn't exist, it is created and initialised. \"\"\" cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials ()","title":"configure_credentials()"},{"location":"reference/#great_ai.ParallelTinyDbDriver","text":"Bases: TracingDatabaseDriver TracingDatabaseDriver with TinyDB as a backend. Saves the database as a JSON into a single file. Highly inefficient on inserting, not advised for production use. A multiprocessing lock protects the database file to avoid parallelisation issues. Source code in great_ai/persistence/parallel_tinydb_driver.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 class ParallelTinyDbDriver ( TracingDatabaseDriver ): \"\"\"TracingDatabaseDriver with TinyDB as a backend. Saves the database as a JSON into a single file. Highly inefficient on inserting, not advised for production use. A multiprocessing lock protects the database file to avoid parallelisation issues. \"\"\" is_production_ready = False path_to_db = Path ( DEFAULT_TRACING_DB_FILENAME ) def save ( self , trace : Trace ) -> str : return self . _safe_execute ( lambda db : db . insert ( trace . dict ())) def save_batch ( self , documents : List [ Trace ]) -> List [ str ]: traces = [ d . dict () for d in documents ] return self . _safe_execute ( lambda db : db . insert_multiple ( traces )) def get ( self , id : str ) -> Optional [ Trace ]: value = self . _safe_execute ( lambda db : db . get ( lambda d : d [ \"trace_id\" ] == id )) if value : value = Trace . parse_obj ( value ) return value def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [] ) -> Tuple [ List [ Trace ], int ]: def does_match ( d : Dict [ str , Any ]) -> bool : return ( not set ( conjunctive_tags ) - set ( d [ \"tags\" ]) and ( since is None or datetime . fromisoformat ( d [ \"created\" ]) >= since ) and ( until is None or datetime . fromisoformat ( d [ \"created\" ]) <= until ) and ( has_feedback is None or has_feedback == ( d [ \"feedback\" ] is not None ) ) ) documents : List [ Trace ] = [ Trace . parse_obj ( t ) for t in self . _safe_execute ( lambda db : db . search ( does_match )) ] if not documents : return [], 0 df = pd . DataFrame ([ d . to_flat_dict () for d in documents ]) for f in conjunctive_filters : operator = f . operator . lower () if operator in operator_mapping : df = df . loc [ getattr ( df [ f . property ], operator_mapping [ f . operator ])( f . value ) ] elif operator == \"contains\" : df = df . loc [ df [ f . property ] . str . contains ( f . value , case = False )] if sort_by : df . sort_values ( [ col . column_id for col in sort_by ], ascending = [ col . direction == \"asc\" for col in sort_by ], inplace = True , ) count = len ( df ) result = df . iloc [ skip :] if take is None else df . iloc [ skip : skip + take ] return [ next ( d for d in documents if d . trace_id == trace_id ) for trace_id in result [ \"trace_id\" ] ], count def update ( self , id : str , new_version : Trace ) -> None : self . _safe_execute ( lambda db : db . update ( new_version . dict (), lambda d : d [ \"trace_id\" ] == id ) ) def delete ( self , id : str ) -> None : self . _safe_execute ( lambda db : db . remove ( lambda d : d [ \"trace_id\" ] == id )) def delete_batch ( self , ids : List [ str ]) -> None : for i in ids : self . delete ( i ) def _safe_execute ( self , func : Callable [[ TinyDB ], Any ]) -> Any : with lock : with TinyDB ( self . path_to_db ) as db : return func ( db )","title":"ParallelTinyDbDriver"},{"location":"reference/large-file/","text":"LargeFile # from great_ai.large_file import * LargeFileLocal # Bases: LargeFileBase Source code in great_ai/large_file/large_file/large_file_local.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 class LargeFileLocal ( LargeFileBase ): def __init__ ( self , name : str , mode : str = \"r\" , * , buffering : int = - 1 , encoding : Optional [ str ] = None , errors : Optional [ str ] = None , newline : Optional [ str ] = None , version : Optional [ int ] = None , keep_last_n : Optional [ int ] = None , ): super () . __init__ ( name , mode , buffering = buffering , encoding = encoding , errors = errors , newline = newline , version = version , keep_last_n = keep_last_n , cache_only_mode = True , ) super () . configure_credentials () def _find_remote_instances ( self ) -> List [ DataInstance ]: return [] def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : raise NotImplementedError () def _upload ( self , local_path : Path , hide_progress : bool ) -> None : pass # the \"upload\" is already done py the parent's caching mechanism def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version (keep_last_n= { self . _keep_last_n } ): { i . remote_path } \" ) i . remote_path . unlink () LargeFileS3 # Bases: LargeFileBase Store large files in S3. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test ') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` Source code in great_ai/large_file/large_file/large_file_s3.py 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 class LargeFileS3 ( LargeFileBase ): \"\"\" Store large files in S3. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test\\n') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` \"\"\" region_name = None access_key_id = None secret_access_key = None bucket_name = None endpoint_url = None @classmethod def configure_credentials ( # type: ignore cls , * , aws_region_name : str , aws_access_key_id : str , aws_secret_access_key : str , large_files_bucket_name : str , aws_endpoint_url : Optional [ str ] = None , ** _ : Any , ) -> None : cls . region_name = aws_region_name cls . access_key_id = aws_access_key_id cls . secret_access_key = aws_secret_access_key cls . bucket_name = large_files_bucket_name cls . endpoint_url = aws_endpoint_url super () . configure_credentials () @cached_property def _client ( self ) -> boto3 . client : if ( self . region_name is None or self . access_key_id is None or self . secret_access_key is None or self . bucket_name is None ): raise ValueError ( \"Please configure the S3 access options by calling LargeFileS3.configure_credentials or set offline_mode=True in the constructor.\" ) return boto3 . client ( \"s3\" , aws_access_key_id = self . access_key_id , aws_secret_access_key = self . secret_access_key , region_name = self . region_name , endpoint_url = self . endpoint_url , ) def _find_remote_instances ( self ) -> List [ DataInstance ]: logger . debug ( f \"Fetching S3 versions of { self . _name } \" ) found_objects = self . _client . list_objects_v2 ( Bucket = self . bucket_name , Prefix = self . _name ) return ( [ DataInstance ( name = o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ 0 ], version = int ( o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = o [ \"Key\" ], ) for o in found_objects [ \"Contents\" ] if o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ 0 ] == self . _name ] if \"Contents\" in found_objects else [] ) def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Downloading { remote_path } from S3\" ) size = self . _client . head_object ( Bucket = self . bucket_name , Key = remote_path )[ \"ContentLength\" ] self . _client . download_file ( Bucket = self . bucket_name , Key = remote_path , Filename = str ( local_path ), Callback = None if hide_progress else DownloadProgressBar ( name = str ( remote_path ), size = size , logger = logger ), ) def _upload ( self , local_path : Path , hide_progress : bool ) -> None : key = f \" { self . _name } / { self . version } \" logger . info ( f \"Uploading { self . _local_name } to S3 as { key } \" ) self . _client . upload_file ( Filename = str ( local_path ), Bucket = self . bucket_name , Key = key , Callback = None if hide_progress else UploadProgressBar ( path = local_path , logger = logger ), ) def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version from S3 (keep_last_n= { self . _keep_last_n } ): { i . remote_path } \" ) self . _client . delete_object ( Bucket = self . bucket_name , Key = i . remote_path ) LargeFileMongo # Bases: LargeFileBase Source code in great_ai/large_file/large_file/large_file_mongo.py 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 class LargeFileMongo ( LargeFileBase ): mongo_connection_string = None mongo_database = None @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () @cached_property def _client ( self ) -> GridFSBucket : if self . mongo_connection_string is None or self . mongo_database is None : raise ValueError ( \"Please configure the MongoDB access options by calling LargeFileMongo.configure_credentials or set offline_mode=True in the constructor.\" ) db : Database = MongoClient ( self . mongo_connection_string )[ self . mongo_database ] return GridFSBucket ( db ) def _find_remote_instances ( self ) -> List [ DataInstance ]: logger . debug ( f \"Fetching Mongo (GridFS) versions of { self . _name } \" ) return [ DataInstance ( name = MONGO_NAME_VERSION_SEPARATOR . join ( f . name . split ( MONGO_NAME_VERSION_SEPARATOR )[: - 1 ] ), version = int ( f . name . split ( MONGO_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = ( f . _id , f . length ), origin = \"mongodb\" , ) for f in self . _client . find ( { \"filename\" : re . compile ( re . escape ( self . _name + MONGO_NAME_VERSION_SEPARATOR ) + \".*\" ) } ) ] def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Downloading { remote_path [ 0 ] } from Mongo (GridFS)\" ) progress = ( DownloadProgressBar ( name = str ( remote_path [ 0 ]), size = remote_path [ 1 ], logger = logger ) if not hide_progress else None ) with self . _client . open_download_stream ( remote_path [ 0 ]) as stream : with open ( local_path , \"wb\" ) as f : while True : content = stream . read ( DEFAULT_CHUNK_SIZE ) f . write ( content ) if progress : progress ( len ( content )) if len ( content ) < DEFAULT_CHUNK_SIZE : break def _upload ( self , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Uploading { local_path } to Mongo (GridFS)\" ) progress = ( UploadProgressBar ( path = local_path , logger = logger ) if not hide_progress else None ) with self . _client . open_upload_stream ( f \" { self . _name }{ MONGO_NAME_VERSION_SEPARATOR }{ self . version } \" ) as stream : with open ( local_path , \"rb\" ) as f : while True : content = f . read ( DEFAULT_CHUNK_SIZE ) stream . write ( content ) if progress : progress ( len ( content )) if len ( content ) < DEFAULT_CHUNK_SIZE : break def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version from MongoDB (GridFS) (keep_last_n= { self . _keep_last_n } ): { i . name }{ MONGO_NAME_VERSION_SEPARATOR }{ i . version } \" ) self . _client . delete ( i . remote_path [ 0 ]) LargeFileBase # Bases: ABC Store large files remotely. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test ') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` Source code in great_ai/large_file/large_file/large_file_base.py 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 class LargeFileBase ( ABC ): \"\"\" Store large files remotely. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test\\n') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` \"\"\" initialized = False cache_path = Path ( \".cache\" ) max_cache_size : Optional [ str ] = \"30GB\" def __init__ ( self , name : str , mode : str = \"r\" , * , buffering : int = - 1 , encoding : Optional [ str ] = None , errors : Optional [ str ] = None , newline : Optional [ str ] = None , version : Optional [ int ] = None , keep_last_n : Optional [ int ] = None , cache_only_mode : bool = False , ): self . _name = name self . _version = version self . _mode = mode self . _keep_last_n = keep_last_n self . _cache_only_mode = cache_only_mode self . _buffering = buffering self . _encoding = encoding self . _errors = errors self . _newline = newline LargeFileBase . cache_path . mkdir ( parents = True , exist_ok = True ) self . _find_instances () self . _check_mode_and_set_version () @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()}) @classmethod def configure_credentials ( cls , ** kwargs : str ) -> None : cls . initialized = True def __enter__ ( self ) -> IO : self . _file : IO [ Any ] = ( tempfile . NamedTemporaryFile ( mode = self . _mode , buffering = self . _buffering , encoding = self . _encoding , newline = self . _newline , errors = self . _errors , delete = False , prefix = \"large_file-\" , ) if \"w\" in self . _mode else open ( self . get (), mode = self . _mode , buffering = self . _buffering , encoding = self . _encoding , newline = self . _newline , errors = self . _errors , ) ) return self . _file def __exit__ ( self , type : Optional [ Type [ BaseException ]], exc : Optional [ BaseException ], traceback : Optional [ TracebackType ], ) -> Literal [ False ]: self . _file . close () if type is None : if \"w\" in self . _mode : self . push ( Path ( self . _file . name )) os . unlink ( self . _file . name ) else : logger . exception ( \"Could not finish operation.\" ) return False @property def _local_name ( self ) -> str : return f \" { self . _name }{ CACHE_NAME_VERSION_SEPARATOR }{ self . version } \" @property def version ( self ) -> int : return cast ( int , self . _version ) @lru_cache ( 1 ) def get ( self , hide_progress : bool = False ) -> Path : remote_path = next ( i . remote_path for i in self . _instances if i . version == self . _version ) destination = self . cache_path / self . _local_name if not destination . exists (): logger . info ( f \"File { self . _local_name } does not exist locally\" ) with tempfile . TemporaryDirectory () as tmp : local_root_path = Path ( tmp ) tmp_file_archive = ( local_root_path / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _download ( remote_path , tmp_file_archive , hide_progress = hide_progress ) logger . info ( f \"Decompressing { self . _local_name } \" ) shutil . unpack_archive ( str ( tmp_file_archive ), tmp , COMPRESSION_ALGORITHM ) shutil . move ( str ( local_root_path / self . _local_name ), str ( destination )) else : logger . info ( f \"File { self . _local_name } found in cache\" ) return destination def push ( self , path : Union [ Path , str ], hide_progress : bool = False ) -> None : if isinstance ( path , str ): path = Path ( path ) with tempfile . TemporaryDirectory () as tmp : if path . is_file (): logger . info ( f \"Copying file for { self . _local_name } \" ) copy : Any = shutil . copy else : logger . info ( f \"Copying directory for { self . _local_name } \" ) copy = shutil . copytree try : # Make local copy in the cache shutil . rmtree ( self . cache_path / self . _local_name , ignore_errors = True ) copy ( str ( path ), str ( self . cache_path / self . _local_name )) except shutil . SameFileError : pass # No worries copy ( str ( path ), str ( Path ( tmp ) / self . _local_name )) with tempfile . TemporaryDirectory () as tmp2 : # A directory has to be zipped and it cannot contain the output of the zipping logger . info ( f \"Compressing { self . _local_name } \" ) shutil . make_archive ( str ( Path ( tmp2 ) / self . _local_name ), COMPRESSION_ALGORITHM , tmp , ) file_to_be_uploaded = ( Path ( tmp2 ) / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _upload ( file_to_be_uploaded , hide_progress = hide_progress ) self . clean_up () def delete ( self ) -> None : self . _keep_last_n = 0 self . _delete_old_remote_versions () def _find_instances ( self ) -> None : if self . _cache_only_mode : self . _instances = self . _find_instances_from_cache () else : self . _instances = self . _find_remote_instances () self . _instances = sorted ( self . _instances , key = lambda i : i . version ) def _find_instances_from_cache ( self ) -> List [ DataInstance ]: logger . info ( f \"Fetching cached versions of { self . _name } \" ) candidates = [ DataInstance ( name = CACHE_NAME_VERSION_SEPARATOR . join ( f . name . split ( CACHE_NAME_VERSION_SEPARATOR )[: - 1 ] ), version = int ( f . name . split ( CACHE_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = f , ) for f in self . cache_path . glob ( f \" { self . _name }{ CACHE_NAME_VERSION_SEPARATOR } *\" ) ] return [ c for c in candidates if c . name == self . _name ] def _check_mode_and_set_version ( self ) -> None : if \"+\" in self . _mode : raise ValueError ( f \"File mode ` { self . _mode } ` is not allowed3, remove the `+`.\" ) if \"w\" in self . _mode : if self . _version is not None : raise ValueError ( \"Providing a version is not allowed in write mode.\" ) self . _version = self . _instances [ - 1 ] . version + 1 if self . _instances else 0 elif \"r\" in self . _mode : if not self . _instances : raise FileNotFoundError ( f \"File { self . _name } not found. No versions are available.\" ) if self . _version is None : self . _version = self . _instances [ - 1 ] . version logger . info ( f \"Latest version of { self . _name } is { self . _version } \" + f \"(from versions: { self . versions_pretty } )\" ) elif self . _version not in [ i . version for i in self . _instances ]: raise FileNotFoundError ( f \"File { self . _name } not found with version { self . _version } . \" + f \"(from versions: { self . versions_pretty } )\" ) else : raise ValueError ( \"Unsupported file mode.\" ) @property def versions_pretty ( self ) -> str : return \", \" . join (( str ( i . version ) for i in self . _instances )) def clean_up ( self ) -> None : self . _delete_old_remote_versions () self . _prune_cache () def _prune_cache ( self ) -> None : self . cache_path . mkdir ( parents = True , exist_ok = True ) if self . max_cache_size is None : return allowed_size = human_readable_to_byte ( self . max_cache_size ) assert allowed_size >= 0 least_recently_read = sorted ( [ f for f in self . cache_path . glob ( \"*\" )], key = lambda f : f . stat () . st_atime ) while sum ( os . path . getsize ( f ) for f in least_recently_read ) > allowed_size : file = least_recently_read . pop ( 0 ) logger . info ( f \"Deleting file from cache to meet quota (max_cache_size= { self . max_cache_size } ): { file } \" ) os . unlink ( file ) @abstractmethod def _find_remote_instances ( self ) -> List [ DataInstance ]: pass @abstractmethod def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : pass @abstractmethod def _upload ( self , local_path : Path , hide_progress : bool ) -> None : pass @abstractmethod def _delete_old_remote_versions ( self ) -> None : pass","title":"LargeFile"},{"location":"reference/large-file/#largefile","text":"from great_ai.large_file import *","title":"LargeFile"},{"location":"reference/large-file/#great_ai.large_file.LargeFileLocal","text":"Bases: LargeFileBase Source code in great_ai/large_file/large_file/large_file_local.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 class LargeFileLocal ( LargeFileBase ): def __init__ ( self , name : str , mode : str = \"r\" , * , buffering : int = - 1 , encoding : Optional [ str ] = None , errors : Optional [ str ] = None , newline : Optional [ str ] = None , version : Optional [ int ] = None , keep_last_n : Optional [ int ] = None , ): super () . __init__ ( name , mode , buffering = buffering , encoding = encoding , errors = errors , newline = newline , version = version , keep_last_n = keep_last_n , cache_only_mode = True , ) super () . configure_credentials () def _find_remote_instances ( self ) -> List [ DataInstance ]: return [] def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : raise NotImplementedError () def _upload ( self , local_path : Path , hide_progress : bool ) -> None : pass # the \"upload\" is already done py the parent's caching mechanism def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version (keep_last_n= { self . _keep_last_n } ): { i . remote_path } \" ) i . remote_path . unlink ()","title":"LargeFileLocal"},{"location":"reference/large-file/#great_ai.large_file.LargeFileS3","text":"Bases: LargeFileBase Store large files in S3. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test ') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` Source code in great_ai/large_file/large_file/large_file_s3.py 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 class LargeFileS3 ( LargeFileBase ): \"\"\" Store large files in S3. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test\\n') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` \"\"\" region_name = None access_key_id = None secret_access_key = None bucket_name = None endpoint_url = None @classmethod def configure_credentials ( # type: ignore cls , * , aws_region_name : str , aws_access_key_id : str , aws_secret_access_key : str , large_files_bucket_name : str , aws_endpoint_url : Optional [ str ] = None , ** _ : Any , ) -> None : cls . region_name = aws_region_name cls . access_key_id = aws_access_key_id cls . secret_access_key = aws_secret_access_key cls . bucket_name = large_files_bucket_name cls . endpoint_url = aws_endpoint_url super () . configure_credentials () @cached_property def _client ( self ) -> boto3 . client : if ( self . region_name is None or self . access_key_id is None or self . secret_access_key is None or self . bucket_name is None ): raise ValueError ( \"Please configure the S3 access options by calling LargeFileS3.configure_credentials or set offline_mode=True in the constructor.\" ) return boto3 . client ( \"s3\" , aws_access_key_id = self . access_key_id , aws_secret_access_key = self . secret_access_key , region_name = self . region_name , endpoint_url = self . endpoint_url , ) def _find_remote_instances ( self ) -> List [ DataInstance ]: logger . debug ( f \"Fetching S3 versions of { self . _name } \" ) found_objects = self . _client . list_objects_v2 ( Bucket = self . bucket_name , Prefix = self . _name ) return ( [ DataInstance ( name = o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ 0 ], version = int ( o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = o [ \"Key\" ], ) for o in found_objects [ \"Contents\" ] if o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ 0 ] == self . _name ] if \"Contents\" in found_objects else [] ) def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Downloading { remote_path } from S3\" ) size = self . _client . head_object ( Bucket = self . bucket_name , Key = remote_path )[ \"ContentLength\" ] self . _client . download_file ( Bucket = self . bucket_name , Key = remote_path , Filename = str ( local_path ), Callback = None if hide_progress else DownloadProgressBar ( name = str ( remote_path ), size = size , logger = logger ), ) def _upload ( self , local_path : Path , hide_progress : bool ) -> None : key = f \" { self . _name } / { self . version } \" logger . info ( f \"Uploading { self . _local_name } to S3 as { key } \" ) self . _client . upload_file ( Filename = str ( local_path ), Bucket = self . bucket_name , Key = key , Callback = None if hide_progress else UploadProgressBar ( path = local_path , logger = logger ), ) def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version from S3 (keep_last_n= { self . _keep_last_n } ): { i . remote_path } \" ) self . _client . delete_object ( Bucket = self . bucket_name , Key = i . remote_path )","title":"LargeFileS3"},{"location":"reference/large-file/#great_ai.large_file.LargeFileMongo","text":"Bases: LargeFileBase Source code in great_ai/large_file/large_file/large_file_mongo.py 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 class LargeFileMongo ( LargeFileBase ): mongo_connection_string = None mongo_database = None @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () @cached_property def _client ( self ) -> GridFSBucket : if self . mongo_connection_string is None or self . mongo_database is None : raise ValueError ( \"Please configure the MongoDB access options by calling LargeFileMongo.configure_credentials or set offline_mode=True in the constructor.\" ) db : Database = MongoClient ( self . mongo_connection_string )[ self . mongo_database ] return GridFSBucket ( db ) def _find_remote_instances ( self ) -> List [ DataInstance ]: logger . debug ( f \"Fetching Mongo (GridFS) versions of { self . _name } \" ) return [ DataInstance ( name = MONGO_NAME_VERSION_SEPARATOR . join ( f . name . split ( MONGO_NAME_VERSION_SEPARATOR )[: - 1 ] ), version = int ( f . name . split ( MONGO_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = ( f . _id , f . length ), origin = \"mongodb\" , ) for f in self . _client . find ( { \"filename\" : re . compile ( re . escape ( self . _name + MONGO_NAME_VERSION_SEPARATOR ) + \".*\" ) } ) ] def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Downloading { remote_path [ 0 ] } from Mongo (GridFS)\" ) progress = ( DownloadProgressBar ( name = str ( remote_path [ 0 ]), size = remote_path [ 1 ], logger = logger ) if not hide_progress else None ) with self . _client . open_download_stream ( remote_path [ 0 ]) as stream : with open ( local_path , \"wb\" ) as f : while True : content = stream . read ( DEFAULT_CHUNK_SIZE ) f . write ( content ) if progress : progress ( len ( content )) if len ( content ) < DEFAULT_CHUNK_SIZE : break def _upload ( self , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Uploading { local_path } to Mongo (GridFS)\" ) progress = ( UploadProgressBar ( path = local_path , logger = logger ) if not hide_progress else None ) with self . _client . open_upload_stream ( f \" { self . _name }{ MONGO_NAME_VERSION_SEPARATOR }{ self . version } \" ) as stream : with open ( local_path , \"rb\" ) as f : while True : content = f . read ( DEFAULT_CHUNK_SIZE ) stream . write ( content ) if progress : progress ( len ( content )) if len ( content ) < DEFAULT_CHUNK_SIZE : break def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version from MongoDB (GridFS) (keep_last_n= { self . _keep_last_n } ): { i . name }{ MONGO_NAME_VERSION_SEPARATOR }{ i . version } \" ) self . _client . delete ( i . remote_path [ 0 ])","title":"LargeFileMongo"},{"location":"reference/large-file/#great_ai.large_file.LargeFileBase","text":"Bases: ABC Store large files remotely. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test ') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` Source code in great_ai/large_file/large_file/large_file_base.py 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 class LargeFileBase ( ABC ): \"\"\" Store large files remotely. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test\\n') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` \"\"\" initialized = False cache_path = Path ( \".cache\" ) max_cache_size : Optional [ str ] = \"30GB\" def __init__ ( self , name : str , mode : str = \"r\" , * , buffering : int = - 1 , encoding : Optional [ str ] = None , errors : Optional [ str ] = None , newline : Optional [ str ] = None , version : Optional [ int ] = None , keep_last_n : Optional [ int ] = None , cache_only_mode : bool = False , ): self . _name = name self . _version = version self . _mode = mode self . _keep_last_n = keep_last_n self . _cache_only_mode = cache_only_mode self . _buffering = buffering self . _encoding = encoding self . _errors = errors self . _newline = newline LargeFileBase . cache_path . mkdir ( parents = True , exist_ok = True ) self . _find_instances () self . _check_mode_and_set_version () @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()}) @classmethod def configure_credentials ( cls , ** kwargs : str ) -> None : cls . initialized = True def __enter__ ( self ) -> IO : self . _file : IO [ Any ] = ( tempfile . NamedTemporaryFile ( mode = self . _mode , buffering = self . _buffering , encoding = self . _encoding , newline = self . _newline , errors = self . _errors , delete = False , prefix = \"large_file-\" , ) if \"w\" in self . _mode else open ( self . get (), mode = self . _mode , buffering = self . _buffering , encoding = self . _encoding , newline = self . _newline , errors = self . _errors , ) ) return self . _file def __exit__ ( self , type : Optional [ Type [ BaseException ]], exc : Optional [ BaseException ], traceback : Optional [ TracebackType ], ) -> Literal [ False ]: self . _file . close () if type is None : if \"w\" in self . _mode : self . push ( Path ( self . _file . name )) os . unlink ( self . _file . name ) else : logger . exception ( \"Could not finish operation.\" ) return False @property def _local_name ( self ) -> str : return f \" { self . _name }{ CACHE_NAME_VERSION_SEPARATOR }{ self . version } \" @property def version ( self ) -> int : return cast ( int , self . _version ) @lru_cache ( 1 ) def get ( self , hide_progress : bool = False ) -> Path : remote_path = next ( i . remote_path for i in self . _instances if i . version == self . _version ) destination = self . cache_path / self . _local_name if not destination . exists (): logger . info ( f \"File { self . _local_name } does not exist locally\" ) with tempfile . TemporaryDirectory () as tmp : local_root_path = Path ( tmp ) tmp_file_archive = ( local_root_path / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _download ( remote_path , tmp_file_archive , hide_progress = hide_progress ) logger . info ( f \"Decompressing { self . _local_name } \" ) shutil . unpack_archive ( str ( tmp_file_archive ), tmp , COMPRESSION_ALGORITHM ) shutil . move ( str ( local_root_path / self . _local_name ), str ( destination )) else : logger . info ( f \"File { self . _local_name } found in cache\" ) return destination def push ( self , path : Union [ Path , str ], hide_progress : bool = False ) -> None : if isinstance ( path , str ): path = Path ( path ) with tempfile . TemporaryDirectory () as tmp : if path . is_file (): logger . info ( f \"Copying file for { self . _local_name } \" ) copy : Any = shutil . copy else : logger . info ( f \"Copying directory for { self . _local_name } \" ) copy = shutil . copytree try : # Make local copy in the cache shutil . rmtree ( self . cache_path / self . _local_name , ignore_errors = True ) copy ( str ( path ), str ( self . cache_path / self . _local_name )) except shutil . SameFileError : pass # No worries copy ( str ( path ), str ( Path ( tmp ) / self . _local_name )) with tempfile . TemporaryDirectory () as tmp2 : # A directory has to be zipped and it cannot contain the output of the zipping logger . info ( f \"Compressing { self . _local_name } \" ) shutil . make_archive ( str ( Path ( tmp2 ) / self . _local_name ), COMPRESSION_ALGORITHM , tmp , ) file_to_be_uploaded = ( Path ( tmp2 ) / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _upload ( file_to_be_uploaded , hide_progress = hide_progress ) self . clean_up () def delete ( self ) -> None : self . _keep_last_n = 0 self . _delete_old_remote_versions () def _find_instances ( self ) -> None : if self . _cache_only_mode : self . _instances = self . _find_instances_from_cache () else : self . _instances = self . _find_remote_instances () self . _instances = sorted ( self . _instances , key = lambda i : i . version ) def _find_instances_from_cache ( self ) -> List [ DataInstance ]: logger . info ( f \"Fetching cached versions of { self . _name } \" ) candidates = [ DataInstance ( name = CACHE_NAME_VERSION_SEPARATOR . join ( f . name . split ( CACHE_NAME_VERSION_SEPARATOR )[: - 1 ] ), version = int ( f . name . split ( CACHE_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = f , ) for f in self . cache_path . glob ( f \" { self . _name }{ CACHE_NAME_VERSION_SEPARATOR } *\" ) ] return [ c for c in candidates if c . name == self . _name ] def _check_mode_and_set_version ( self ) -> None : if \"+\" in self . _mode : raise ValueError ( f \"File mode ` { self . _mode } ` is not allowed3, remove the `+`.\" ) if \"w\" in self . _mode : if self . _version is not None : raise ValueError ( \"Providing a version is not allowed in write mode.\" ) self . _version = self . _instances [ - 1 ] . version + 1 if self . _instances else 0 elif \"r\" in self . _mode : if not self . _instances : raise FileNotFoundError ( f \"File { self . _name } not found. No versions are available.\" ) if self . _version is None : self . _version = self . _instances [ - 1 ] . version logger . info ( f \"Latest version of { self . _name } is { self . _version } \" + f \"(from versions: { self . versions_pretty } )\" ) elif self . _version not in [ i . version for i in self . _instances ]: raise FileNotFoundError ( f \"File { self . _name } not found with version { self . _version } . \" + f \"(from versions: { self . versions_pretty } )\" ) else : raise ValueError ( \"Unsupported file mode.\" ) @property def versions_pretty ( self ) -> str : return \", \" . join (( str ( i . version ) for i in self . _instances )) def clean_up ( self ) -> None : self . _delete_old_remote_versions () self . _prune_cache () def _prune_cache ( self ) -> None : self . cache_path . mkdir ( parents = True , exist_ok = True ) if self . max_cache_size is None : return allowed_size = human_readable_to_byte ( self . max_cache_size ) assert allowed_size >= 0 least_recently_read = sorted ( [ f for f in self . cache_path . glob ( \"*\" )], key = lambda f : f . stat () . st_atime ) while sum ( os . path . getsize ( f ) for f in least_recently_read ) > allowed_size : file = least_recently_read . pop ( 0 ) logger . info ( f \"Deleting file from cache to meet quota (max_cache_size= { self . max_cache_size } ): { file } \" ) os . unlink ( file ) @abstractmethod def _find_remote_instances ( self ) -> List [ DataInstance ]: pass @abstractmethod def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : pass @abstractmethod def _upload ( self , local_path : Path , hide_progress : bool ) -> None : pass @abstractmethod def _delete_old_remote_versions ( self ) -> None : pass","title":"LargeFileBase"},{"location":"reference/utilities/","text":"Utilities # from great_ai.utilities import * NLP tools # Well-tested tools that can be used in production with confidence. The toolbox of feature-extraction functions is expected to grow to cover other domains as well. clean ( text , ignore_xml = False , ignore_latex = False , remove_brackets = False , convert_to_ascii = False ) # Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean ( '<h2 color=\"red\">Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 &lt;3</h2>' ) 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean ( ... '<h2 color=\"red\">Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 &lt;3</h2>' , ... convert_to_ascii = True ... ) 'Bjorn is happy <3' >>> clean ( ... '<h2 color=\"red\">Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 &lt;3</h2>' , ... ignore_xml = True ... ) '<h2 color=\"red\">Bj\u00f6rn is happy \ud83d\ude42 lt;3</h2>' Parameters: Name Type Description Default text str Text to be cleaned. required ignore_xml bool Do not process/remove XML-tags. False ignore_latex bool Do not process/remove LaTeX-tags. False remove_brackets bool Do not remove brackets ([]) False convert_to_ascii bool Strip (or convert) non-ascii characters. False Returns: Type Description str The cleaned input text with sensibly collapsed whitespace and optionally no markup. Source code in great_ai/utilities/clean.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 def clean ( text : str , ignore_xml : bool = False , ignore_latex : bool = False , remove_brackets : bool = False , convert_to_ascii : bool = False , ) -> str : \"\"\"Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean('<h2 color=\"red\">Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 &lt;3</h2>') 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean( ... '<h2 color=\"red\">Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 &lt;3</h2>', ... convert_to_ascii=True ... ) 'Bjorn is happy <3' >>> clean( ... '<h2 color=\"red\">Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 &lt;3</h2>', ... ignore_xml=True ... ) '<h2 color=\"red\">Bj\u00f6rn is happy \ud83d\ude42 lt;3</h2>' Args: text: Text to be cleaned. ignore_xml: Do not process/remove XML-tags. ignore_latex: Do not process/remove LaTeX-tags. remove_brackets: Do not remove brackets ([]) convert_to_ascii: Strip (or convert) non-ascii characters. Returns: The cleaned input text with sensibly collapsed whitespace and optionally no markup. \"\"\" if not ignore_xml : text = re . sub ( r \"<[^>]*>\" , \" \" , text ) text = html . unescape ( text ) if not ignore_latex : text = text . replace ( \"%\" , \" \\\\ %\" ) # escape LaTeX comments before parsing as LaTeX try : text = latex . latex_to_text ( text , tolerant_parsing = True , strict_braces = False ) text = text . replace ( \" %s \" , \" \" ) except : logger . exception ( \"Latex parsing error\" ) if convert_to_ascii : text = unicodedata . normalize ( \"NFKD\" , text ) try : text . encode ( \"ASCII\" , errors = \"strict\" ) except UnicodeEncodeError : text = \"\" . join ([ c for c in text if not unicodedata . combining ( c )]) text = unidecode . unidecode ( text ) text = re . sub ( r \"\\b[a-zA-Z](?:[\\t ]+[a-zA-Z]\\b)+\" , lambda m : re . sub ( r \"[\\t ]\" , \"\" , m [ 0 ]), text ) # A R T I C L E => ARTICLE if remove_brackets : text = re . sub ( r \"\\[[^\\]]*\\]\" , \" \" , text ) # fix hypens: break- word => break-word text = re . sub ( r \"(\\S)-\\s+\" , r \"\\1-\" , text ) text = re . sub ( r \"\\s+-(\\S)\" , r \"-\\1\" , text ) # collapse whitespace text = re . sub ( r \"\\s+\" , \" \" , text ) # fix punctuation text = re . sub ( rf \" ([ { joined_left_punctuations } ])\" , r \"\\1\" , text ) text = re . sub ( rf \"([ { joined_right_punctuations } ]) \" , r \"\\1\" , text ) text = text . strip () return text get_sentences ( text , ignore_partial = False , true_case = False , remove_punctuation = False ) # Return the list of sentences found in the input text. Use syntok to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences ( 'This is a sentence. This is a half' ) ['This is a sentence.', 'This is a half'] >>> get_sentences ( 'This is a sentence. This is a half' , ignore_partial = True ) ['This is a sentence.'] >>> get_sentences ( 'I like Apple.' , true_case = True , remove_punctuation = True ) ['i like Apple'] Parameters: Name Type Description Default text str Text to be segmented into sentences. required ignore_partial bool Filter out sentences that are not capitalised/don't end with a punctuation. False true_case bool Crude method: lowercase the first word of each sentence. False remove_punctuation bool Remove all kinds of punctuation. False Returns: Type Description List [ str ] The found sentences (with partial sentences optionally filtered out). Source code in great_ai/utilities/get_sentences.py 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 def get_sentences ( text : str , ignore_partial : bool = False , true_case : bool = False , remove_punctuation : bool = False , ) -> List [ str ]: \"\"\"Return the list of sentences found in the input text. Use [syntok](https://github.com/fnl/syntok) to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences('This is a sentence. This is a half') ['This is a sentence.', 'This is a half'] >>> get_sentences('This is a sentence. This is a half', ignore_partial=True) ['This is a sentence.'] >>> get_sentences('I like Apple.', true_case=True, remove_punctuation=True) ['i like Apple'] Args: text: Text to be segmented into sentences. ignore_partial: Filter out sentences that are not capitalised/don't end with a punctuation. true_case: Crude method: lowercase the first word of each sentence. remove_punctuation: Remove all kinds of punctuation. Returns: The found sentences (with partial sentences optionally filtered out). \"\"\" tokenizer = Tokenizer ( emit_hyphen_or_underscore_sep = True , replace_not_contraction = False ) token_stream = tokenizer . tokenize ( text ) def process ( sentence : str ) -> str : if true_case : sentence = sentence [ 0 ] . lower () + sentence [ 1 :] # very crude method if remove_punctuation : sentence = re . sub ( punctuations_pattern , \" \" , sentence ) return sentence . strip () sentences = [ process ( tokenizer . to_text ( sentence )) for sentence in segment ( token_stream ) ] if ignore_partial : sentences = [ sentence for sentence in sentences if sentence [ 0 ] . isupper () and sentence [ - 1 ] in sentence_ending_punctuations ] return sentences predict_language ( text ) # Predict the language code from text. A thin wrapper over langcodes for convenient language tagging. Examples: >>> predict_language ( 'This is a sentence.' ) 'en' Parameters: Name Type Description Default text Optional [ str ] Text used for prediction. required Returns: Type Description str The predicted language code (en, en-US) or und if a prediction could not be made. Source code in great_ai/utilities/language/predict_language.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def predict_language ( text : Optional [ str ]) -> str : \"\"\"Predict the language code from text. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> predict_language('This is a sentence.') 'en' Args: text: Text used for prediction. Returns: The predicted language code (en, en-US) or `und` if a prediction could not be made. \"\"\" if not text : return Language . make () . to_tag () try : language_code = detect ( text ) except LangDetectException : return Language . make () . to_tag () return Language . get ( language_code ) . to_tag () english_name_of_language ( language_code ) # Human-friendly English name of language from its language_code . A thin wrapper over langcodes for convenient language tagging. Examples: >>> english_name_of_language ( 'en-US' ) 'English (United States)' >>> english_name_of_language ( 'und' ) 'Unknown language' Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by great_ai.utilities.language.predict_language.predict_language . required Returns: Type Description str English name of language. Source code in great_ai/utilities/language/english_name_of_language.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 def english_name_of_language ( language_code : Optional [ str ]) -> str : \"\"\"Human-friendly English name of language from its `language_code`. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> english_name_of_language('en-US') 'English (United States)' >>> english_name_of_language('und') 'Unknown language' Args: language_code: Language code, for example, returned by [great_ai.utilities.language.predict_language.predict_language][]. Returns: English name of language. \"\"\" if not language_code : language_code = \"und\" return Language . get ( language_code ) . display_name () is_english ( language_code ) # Decide whether the language_code is of an English language. A thin wrapper over langcodes for convenient language tagging. Examples: >>> is_english ( 'en-US' ) True >>> is_english ( None ) False >>> is_english ( 'und' ) False Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by ` great_ai.utilities.language.predict_language.predict_language . required Returns: Type Description bool Boolean indicating whether it's English. Source code in great_ai/utilities/language/is_english.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def is_english ( language_code : Optional [ str ]) -> bool : \"\"\"Decide whether the `language_code` is of an English language. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> is_english('en-US') True >>> is_english(None) False >>> is_english('und') False Args: language_code: Language code, for example, returned by `[great_ai.utilities.language.predict_language.predict_language][]. Returns: Boolean indicating whether it's English. \"\"\" if not language_code : language_code = \"und\" language_code = standardize_tag ( language_code ) return tag_distance ( language_code , \"en\" ) < 15 evaluate_ranking ( expected , actual_scores , target_recall , title = '' , disable_interpolation = False , axes = None , output_svg = None , reverse_order = False , plot = True ) # Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's PR-curve Parameters: Name Type Description Default expected List [ T ] Expected ordering of the elements (rank if it's an integer, alphabetical if a string) required actual_scores List [ float ] Actual ranking scores (need not be on the same scale as expected ) required title Optional [ str ] Title of the plot. '' disable_interpolation bool Do not interpolate. False axes Optional [ plt . Axes ] Matplotlib axes for ploting inside a subplot. None output_svg Optional [ Path ] If specified, save the chart as an svg to the given Path. None reverse_order bool Reverse the ranking specified by expected . False plot bool Display a plot on the screen. True Returns: Type Description Dict [ T , float ] Precision values at given recall. Source code in great_ai/utilities/evaluate_ranking/evaluate_ranking.py 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 def evaluate_ranking ( expected : List [ T ], actual_scores : List [ float ], target_recall : float , title : Optional [ str ] = \"\" , disable_interpolation : bool = False , axes : Optional [ plt . Axes ] = None , output_svg : Optional [ Path ] = None , reverse_order : bool = False , plot : bool = True , ) -> Dict [ T , float ]: \"\"\"Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's [PR-curve](https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py) Args: expected: Expected ordering of the elements (rank if it's an integer, alphabetical if a string) actual_scores: Actual ranking scores (need not be on the same scale as `expected`) title: Title of the plot. disable_interpolation: Do not interpolate. axes: Matplotlib axes for ploting inside a subplot. output_svg: If specified, save the chart as an svg to the given Path. reverse_order: Reverse the ranking specified by `expected`. plot: Display a plot on the screen. Returns: Precision values at given recall. \"\"\" assert 0 <= target_recall <= 1 if plot and axes is None : fig = plt . figure ( figsize = ( 20 , 10 )) fig . patch . set_facecolor ( \"white\" ) ax = plt . axes () else : ax = axes classes = sorted ( unique ( expected ), reverse = reverse_order ) str_classes = [ str ( c ) for c in classes ] with matplotlib . rc_context ({ \"font.size\" : 20 }): if plot : ax . set_xmargin ( 0 ) draw_f1_iso_lines ( axes = ax ) results : Dict [ T , float ] = {} for i in range ( len ( classes ) - 1 ): binarized_expected = [ ( v < classes [ i ]) if reverse_order else ( v > classes [ i ]) for v in expected ] precision , recall , _ = precision_recall_curve ( binarized_expected , actual_scores ) if not disable_interpolation : for j in range ( 1 , len ( precision )): precision [ j ] = max ( precision [ j - 1 ], precision [ j ]) closest_recall_index = np . argmin ( np . abs ( recall - target_recall )) precision_at_closest_recall = precision [ closest_recall_index ] average_precision = average_precision_score ( binarized_expected , actual_scores ) results [ classes [ i ]] = precision_at_closest_recall if plot : ax . plot ( recall , precision , label = f \" { '|' . join ( str_classes [: i + 1 ]) } \u2194 { '|' . join ( str_classes [ i + 1 :]) } (P@ { target_recall : .2f } = { precision_at_closest_recall : .2f } , AP= { average_precision : .2f } )\" , ) if plot : ax . legend ( loc = \"upper right\" ) ax . axvline ( x = target_recall , linestyle = \"--\" , color = \"#55c6bb\" , linewidth = 2.0 ) if title is None : title = \"Ranking evaluation\" ax . set_title ( f ' { title } ( { \" < \" . join ( str_classes ) } )' , pad = 20 ) ax . set_xlabel ( \"Recall\" ) ax . set_ylabel ( \"Precision\" ) ax . set_xticks ([ target_recall ] + sorted ( ax . get_xticks ())) if plot and output_svg is None : if axes is None : plt . show () elif output_svg : plt . savefig ( output_svg , format = \"svg\" ) return results Parallel processing # Multiprocessing and multithreading-based parallelism with support for async functions. Its main purpose is to implement great_ai.GreatAI.process_batch , however, the parallel processing functions are also convenient for covering other types of mapping needs with a friendlier API than joblib or multiprocess . simple_parallel_map ( func , input_values , * , chunk_size = None , concurrency = None ) # Execute a map operation on an list mimicking the API of the built-in map() . A thin-wrapper over parallel_map . For more options, consult the documentation of parallel_map . Examples: >>> import math >>> list ( simple_parallel_map ( math . sqrt , [ 9 , 4 , 1 ])) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Sequence [ T ] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None Returns: Type Description List [ V ] An iterable of results obtained from applying func to each input value. Raises: Type Description WorkerException If there was an error in the func function in a background process. Source code in great_ai/utilities/parallel_map/simple_parallel_map.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 def simple_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Sequence [ T ], * , chunk_size : Optional [ int ] = None , concurrency : Optional [ int ] = None , ) -> List [ V ]: \"\"\"Execute a map operation on an list mimicking the API of the built-in `map()`. A thin-wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. For more options, consult the documentation of [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. Examples: >>> import math >>> list(simple_parallel_map(math.sqrt, [9, 4, 1])) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. Returns: An iterable of results obtained from applying `func` to each input value. Raises: WorkerException: If there was an error in the `func` function in a background process. \"\"\" input_values = list ( input_values ) # in case the input is mistakenly not a sequence generator = parallel_map ( func = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) return list ( tqdm ( generator , total = len ( input_values ), ) ) parallel_map ( func , input_values , * , chunk_size = None , ignore_exceptions = False , concurrency = None , unordered = False ) # Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and async map functions. The func function is serialised with dill . Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some startup cost. Each process processes a single chunk at once. Examples: >>> import math >>> list ( parallel_map ( math . sqrt , [ 9 , 4 , 1 ], concurrency = 2 )) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background process and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/parallel_map.py 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 def parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and `async` map functions. The `func` function is serialised with `dill`. Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some startup cost. Each process processes a single chunk at once. Examples: >>> import math >>> list(parallel_map(math.sqrt, [9, 4, 1], concurrency=2)) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background process and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) ctx = ( mp . get_context ( \"fork\" ) if \"fork\" in mp . get_all_start_methods () else mp . get_context ( \"spawn\" ) ) ctx . freeze_support () manager = ctx . Manager () input_queue = manager . Queue ( config . concurrency * 2 ) output_queue = manager . Queue ( config . concurrency * 2 ) should_stop = ctx . Event () serialized_map_function = dill . dumps ( func , byref = True , recurse = False ) processes = [ ctx . Process ( name = f \"parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = serialized_map_function , ), ) for i in range ( config . concurrency ) ] for p in processes : p . start () try : yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () except WorkerException : should_stop . set () raise except Exception : for p in processes : p . terminate () p . kill () raise finally : for p in processes : p . join () # terminated processes have to be joined else they remain zombies p . close () manager . shutdown () threaded_parallel_map ( func , input_values , * , chunk_size = None , ignore_exceptions = False , concurrency = None , unordered = False ) # Execute a map operation on an iterable stream. Similar to parallel_map but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and async map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list ( threaded_parallel_map ( lambda x : x ** 2 , [ 1 , 2 , 3 ])) [1, 4, 9] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new threads to start. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background thread and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/threaded_parallel_map.py 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 def threaded_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. Similar to [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and `async` map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list(threaded_parallel_map(lambda x: x ** 2, [1, 2, 3])) [1, 4, 9] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new threads to start. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background thread and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) input_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) output_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) should_stop = threading . Event () threads = [ threading . Thread ( name = f \"threaded_parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = func , ), ) for i in range ( config . concurrency ) ] for t in threads : t . start () yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () for t in threads : t . join ( 1 ) Composable parallel processing # Because both threaded_parallel_map and parallel_map have a streaming interface, it is easy to compose them and end up with, for example, a process for each CPU core with its own thread-pool or event-loop. Longer pipelines are also easy to imagine. The chunking methods help in these compositions. chunk ( values , chunk_size ) # Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length chunk_size except the last one the length of which is between 1 and chunk_size . Useful for parallel processing. Examples: >>> list ( chunk ( range ( 10 ), chunk_size = 3 )) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Parameters: Name Type Description Default values Iterable [ T ] The stream of items to pack into chunks. required chunk_size int Desired length of each (but the last) chunk. required Yields: Type Description Iterable [ List [ T ]] The next chunk. Source code in great_ai/utilities/chunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def chunk ( values : Iterable [ T ], chunk_size : int ) -> Iterable [ List [ T ]]: \"\"\"Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length `chunk_size` except the last one the length of which is between 1 and `chunk_size`. Useful for parallel processing. Examples: >>> list(chunk(range(10), chunk_size=3)) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Args: values: The stream of items to pack into chunks. chunk_size: Desired length of each (but the last) chunk. Yields: The next chunk. \"\"\" assert chunk_size >= 1 result : List [ T ] = [] for v in values : result . append ( v ) if len ( result ) == chunk_size : yield result result = [] if len ( result ) > 0 : yield result unchunk ( chunks ) # Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of chunk . Useful for parallel processing. Similar to itertools.chain but ignores None chunks. Examples: >>> list ( unchunk ([[ 0 , 1 , 2 ], [ 3 , 4 , 5 ], [ 6 , 7 , 8 ], [ 9 ]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Parameters: Name Type Description Default chunks Iterable [ Optional [ Iterable [ T ]]] Stream of chunks to unpack. required Yields: Type Description Iterable [ T ] The next item in the flattened iterable. Source code in great_ai/utilities/unchunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 def unchunk ( chunks : Iterable [ Optional [ Iterable [ T ]]]) -> Iterable [ T ]: \"\"\"Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of [chunk][great_ai.utilities.chunk.chunk]. Useful for parallel processing. Similar to itertools.chain but ignores `None` chunks. Examples: >>> list(unchunk([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Args: chunks: Stream of chunks to unpack. Yields: The next item in the flattened iterable. \"\"\" for chunk in chunks : if chunk is not None : yield from chunk Operations # ConfigFile # Bases: Mapping [ str , str ] A small and safe INI -style configuration loader with dict and ENV support. The values can be accessed using both dot- and index-notation. It is compatible with the dict interface. File format example: # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV : MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. Examples: >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . zeroth_key 'test' >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . second_key Traceback (most recent call last): ... KeyError : 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile ( 'tests/utilities/data/simple.conf' ) >>> { ** a } {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} Source code in great_ai/utilities/config_file/config_file.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 class ConfigFile ( Mapping [ str , str ]): \"\"\"A small and safe `INI`-style configuration loader with `dict` and `ENV` support. The values can be accessed using both dot- and index-notation. It is compatible with the `dict` interface. File format example: ```toml # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV:MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. ``` Examples: >>> ConfigFile('tests/utilities/data/simple.conf') ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile('tests/utilities/data/simple.conf').zeroth_key 'test' >>> ConfigFile('tests/utilities/data/simple.conf').second_key Traceback (most recent call last): ... KeyError: 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile('tests/utilities/data/simple.conf') >>> {**a} {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} \"\"\" ENVIRONMENT_VARIABLE_KEY_PREFIX = \"ENV\" def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse () @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path def _parse ( self ) -> None : with open ( self . _path , encoding = \"utf-8\" ) as f : lines : str = f . read () matches = pattern . findall ( lines ) for key , * values in matches : try : value = next ( v for v in values if v ) except StopIteration : raise ParseError ( f \"\"\"Cannot parse config file ( { self . _path . absolute () } ), error at key ` { key } `\"\"\" ) already_exists = key in self . _key_values if already_exists and not value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): raise KeyError ( f \"Key ` { key } ` has been already defined and its value is ` { self . _key_values [ key ] } `\" ) if value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): _ , value = value . split ( \":\" ) if value not in os . environ : issue = f \"\"\"The value of ` { key } ` contains the \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } ` prefix but ` { value } ` is not defined as an environment variable\"\"\" if already_exists : logger . warning ( f \"\"\" { issue } , using the default value defined above (` { self . _key_values [ key ] } `)\"\"\" ) continue elif self . _ignore_missing : logger . warning ( issue ) else : raise ValueError ( f \" { issue } and no default value has been provided\" ) else : value = os . environ [ value ] self . _key_values [ key ] = value def __getattr__ ( self , key : str ) -> str : if key in self . _key_values : return self . _key_values [ key ] raise KeyError ( f \"Key ` { key } ` is not found in configuration file ( { self . _path . absolute () } )\" ) __getitem__ = __getattr__ def __iter__ ( self ) -> Iterator [ str ]: return iter ( self . _key_values ) def __len__ ( self ) -> int : return len ( self . _key_values ) def keys ( self ) -> KeysView [ str ]: return self . _key_values . keys () def values ( self ) -> ValuesView [ str ]: return self . _key_values . values () def items ( self ) -> ItemsView [ str , str ]: return self . _key_values . items () def __repr__ ( self ) -> str : return f \" { type ( self ) . __name__ } (path= { self . _path . as_posix () } ) { self . _key_values } \" __init__ ( path , * , ignore_missing = False ) # Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Parameters: Name Type Description Default path Union [ Path , str ] Local path of the configuration file. required ignore_missing bool Don't raise an exception on missing environment variables. False Raises: Type Description FileNotFoundError If there is no file at the specified path. ParseError If the provided file does not conform to the expected format. KeyError If there is duplication in the keys. ValueError If an environment variable is referenced but it is not set in the system and ignore_missing=False . Source code in great_ai/utilities/config_file/config_file.py 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse () path () property # Original path from where the configuration was loaded. Source code in great_ai/utilities/config_file/config_file.py 83 84 85 86 @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path get_logger ( name , level = logging . INFO , disable_colors = False ) # Return a customised logger used throughout the GreatAI codebase. Uses colors, and only prints timestamps when not running inside notebook. Source code in great_ai/utilities/logger/get_logger.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def get_logger ( name : str , level : int = logging . INFO , disable_colors : bool = False ) -> logging . Logger : \"\"\"Return a customised logger used throughout the GreatAI codebase. Uses colors, and only prints timestamps when not running inside notebook. \"\"\" if name not in loggers : logger = logging . getLogger ( name ) logger . setLevel ( level ) try : get_ipython () # type: ignore log_format = \" %(message)s \" except NameError : # will fail outside of a notebook https://ipython.org/ log_format = \" %(asctime)s | %(levelname)8s | %(message)s \" stdout_handler = logging . StreamHandler () stdout_handler . setLevel ( level ) if not disable_colors : stdout_handler . setFormatter ( CustomFormatter ( log_format )) logger . addHandler ( stdout_handler ) loggers [ name ] = logger return loggers [ name ]","title":"Utilities"},{"location":"reference/utilities/#utilities","text":"from great_ai.utilities import *","title":"Utilities"},{"location":"reference/utilities/#nlp-tools","text":"Well-tested tools that can be used in production with confidence. The toolbox of feature-extraction functions is expected to grow to cover other domains as well.","title":"NLP tools"},{"location":"reference/utilities/#great_ai.utilities.clean.clean","text":"Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean ( '<h2 color=\"red\">Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 &lt;3</h2>' ) 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean ( ... '<h2 color=\"red\">Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 &lt;3</h2>' , ... convert_to_ascii = True ... ) 'Bjorn is happy <3' >>> clean ( ... '<h2 color=\"red\">Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 &lt;3</h2>' , ... ignore_xml = True ... ) '<h2 color=\"red\">Bj\u00f6rn is happy \ud83d\ude42 lt;3</h2>' Parameters: Name Type Description Default text str Text to be cleaned. required ignore_xml bool Do not process/remove XML-tags. False ignore_latex bool Do not process/remove LaTeX-tags. False remove_brackets bool Do not remove brackets ([]) False convert_to_ascii bool Strip (or convert) non-ascii characters. False Returns: Type Description str The cleaned input text with sensibly collapsed whitespace and optionally no markup. Source code in great_ai/utilities/clean.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 def clean ( text : str , ignore_xml : bool = False , ignore_latex : bool = False , remove_brackets : bool = False , convert_to_ascii : bool = False , ) -> str : \"\"\"Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean('<h2 color=\"red\">Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 &lt;3</h2>') 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean( ... '<h2 color=\"red\">Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 &lt;3</h2>', ... convert_to_ascii=True ... ) 'Bjorn is happy <3' >>> clean( ... '<h2 color=\"red\">Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 &lt;3</h2>', ... ignore_xml=True ... ) '<h2 color=\"red\">Bj\u00f6rn is happy \ud83d\ude42 lt;3</h2>' Args: text: Text to be cleaned. ignore_xml: Do not process/remove XML-tags. ignore_latex: Do not process/remove LaTeX-tags. remove_brackets: Do not remove brackets ([]) convert_to_ascii: Strip (or convert) non-ascii characters. Returns: The cleaned input text with sensibly collapsed whitespace and optionally no markup. \"\"\" if not ignore_xml : text = re . sub ( r \"<[^>]*>\" , \" \" , text ) text = html . unescape ( text ) if not ignore_latex : text = text . replace ( \"%\" , \" \\\\ %\" ) # escape LaTeX comments before parsing as LaTeX try : text = latex . latex_to_text ( text , tolerant_parsing = True , strict_braces = False ) text = text . replace ( \" %s \" , \" \" ) except : logger . exception ( \"Latex parsing error\" ) if convert_to_ascii : text = unicodedata . normalize ( \"NFKD\" , text ) try : text . encode ( \"ASCII\" , errors = \"strict\" ) except UnicodeEncodeError : text = \"\" . join ([ c for c in text if not unicodedata . combining ( c )]) text = unidecode . unidecode ( text ) text = re . sub ( r \"\\b[a-zA-Z](?:[\\t ]+[a-zA-Z]\\b)+\" , lambda m : re . sub ( r \"[\\t ]\" , \"\" , m [ 0 ]), text ) # A R T I C L E => ARTICLE if remove_brackets : text = re . sub ( r \"\\[[^\\]]*\\]\" , \" \" , text ) # fix hypens: break- word => break-word text = re . sub ( r \"(\\S)-\\s+\" , r \"\\1-\" , text ) text = re . sub ( r \"\\s+-(\\S)\" , r \"-\\1\" , text ) # collapse whitespace text = re . sub ( r \"\\s+\" , \" \" , text ) # fix punctuation text = re . sub ( rf \" ([ { joined_left_punctuations } ])\" , r \"\\1\" , text ) text = re . sub ( rf \"([ { joined_right_punctuations } ]) \" , r \"\\1\" , text ) text = text . strip () return text","title":"clean()"},{"location":"reference/utilities/#great_ai.utilities.get_sentences.get_sentences","text":"Return the list of sentences found in the input text. Use syntok to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences ( 'This is a sentence. This is a half' ) ['This is a sentence.', 'This is a half'] >>> get_sentences ( 'This is a sentence. This is a half' , ignore_partial = True ) ['This is a sentence.'] >>> get_sentences ( 'I like Apple.' , true_case = True , remove_punctuation = True ) ['i like Apple'] Parameters: Name Type Description Default text str Text to be segmented into sentences. required ignore_partial bool Filter out sentences that are not capitalised/don't end with a punctuation. False true_case bool Crude method: lowercase the first word of each sentence. False remove_punctuation bool Remove all kinds of punctuation. False Returns: Type Description List [ str ] The found sentences (with partial sentences optionally filtered out). Source code in great_ai/utilities/get_sentences.py 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 def get_sentences ( text : str , ignore_partial : bool = False , true_case : bool = False , remove_punctuation : bool = False , ) -> List [ str ]: \"\"\"Return the list of sentences found in the input text. Use [syntok](https://github.com/fnl/syntok) to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences('This is a sentence. This is a half') ['This is a sentence.', 'This is a half'] >>> get_sentences('This is a sentence. This is a half', ignore_partial=True) ['This is a sentence.'] >>> get_sentences('I like Apple.', true_case=True, remove_punctuation=True) ['i like Apple'] Args: text: Text to be segmented into sentences. ignore_partial: Filter out sentences that are not capitalised/don't end with a punctuation. true_case: Crude method: lowercase the first word of each sentence. remove_punctuation: Remove all kinds of punctuation. Returns: The found sentences (with partial sentences optionally filtered out). \"\"\" tokenizer = Tokenizer ( emit_hyphen_or_underscore_sep = True , replace_not_contraction = False ) token_stream = tokenizer . tokenize ( text ) def process ( sentence : str ) -> str : if true_case : sentence = sentence [ 0 ] . lower () + sentence [ 1 :] # very crude method if remove_punctuation : sentence = re . sub ( punctuations_pattern , \" \" , sentence ) return sentence . strip () sentences = [ process ( tokenizer . to_text ( sentence )) for sentence in segment ( token_stream ) ] if ignore_partial : sentences = [ sentence for sentence in sentences if sentence [ 0 ] . isupper () and sentence [ - 1 ] in sentence_ending_punctuations ] return sentences","title":"get_sentences()"},{"location":"reference/utilities/#great_ai.utilities.language.predict_language.predict_language","text":"Predict the language code from text. A thin wrapper over langcodes for convenient language tagging. Examples: >>> predict_language ( 'This is a sentence.' ) 'en' Parameters: Name Type Description Default text Optional [ str ] Text used for prediction. required Returns: Type Description str The predicted language code (en, en-US) or und if a prediction could not be made. Source code in great_ai/utilities/language/predict_language.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def predict_language ( text : Optional [ str ]) -> str : \"\"\"Predict the language code from text. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> predict_language('This is a sentence.') 'en' Args: text: Text used for prediction. Returns: The predicted language code (en, en-US) or `und` if a prediction could not be made. \"\"\" if not text : return Language . make () . to_tag () try : language_code = detect ( text ) except LangDetectException : return Language . make () . to_tag () return Language . get ( language_code ) . to_tag ()","title":"predict_language()"},{"location":"reference/utilities/#great_ai.utilities.language.english_name_of_language.english_name_of_language","text":"Human-friendly English name of language from its language_code . A thin wrapper over langcodes for convenient language tagging. Examples: >>> english_name_of_language ( 'en-US' ) 'English (United States)' >>> english_name_of_language ( 'und' ) 'Unknown language' Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by great_ai.utilities.language.predict_language.predict_language . required Returns: Type Description str English name of language. Source code in great_ai/utilities/language/english_name_of_language.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 def english_name_of_language ( language_code : Optional [ str ]) -> str : \"\"\"Human-friendly English name of language from its `language_code`. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> english_name_of_language('en-US') 'English (United States)' >>> english_name_of_language('und') 'Unknown language' Args: language_code: Language code, for example, returned by [great_ai.utilities.language.predict_language.predict_language][]. Returns: English name of language. \"\"\" if not language_code : language_code = \"und\" return Language . get ( language_code ) . display_name ()","title":"english_name_of_language()"},{"location":"reference/utilities/#great_ai.utilities.language.is_english.is_english","text":"Decide whether the language_code is of an English language. A thin wrapper over langcodes for convenient language tagging. Examples: >>> is_english ( 'en-US' ) True >>> is_english ( None ) False >>> is_english ( 'und' ) False Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by ` great_ai.utilities.language.predict_language.predict_language . required Returns: Type Description bool Boolean indicating whether it's English. Source code in great_ai/utilities/language/is_english.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def is_english ( language_code : Optional [ str ]) -> bool : \"\"\"Decide whether the `language_code` is of an English language. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> is_english('en-US') True >>> is_english(None) False >>> is_english('und') False Args: language_code: Language code, for example, returned by `[great_ai.utilities.language.predict_language.predict_language][]. Returns: Boolean indicating whether it's English. \"\"\" if not language_code : language_code = \"und\" language_code = standardize_tag ( language_code ) return tag_distance ( language_code , \"en\" ) < 15","title":"is_english()"},{"location":"reference/utilities/#great_ai.utilities.evaluate_ranking.evaluate_ranking.evaluate_ranking","text":"Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's PR-curve Parameters: Name Type Description Default expected List [ T ] Expected ordering of the elements (rank if it's an integer, alphabetical if a string) required actual_scores List [ float ] Actual ranking scores (need not be on the same scale as expected ) required title Optional [ str ] Title of the plot. '' disable_interpolation bool Do not interpolate. False axes Optional [ plt . Axes ] Matplotlib axes for ploting inside a subplot. None output_svg Optional [ Path ] If specified, save the chart as an svg to the given Path. None reverse_order bool Reverse the ranking specified by expected . False plot bool Display a plot on the screen. True Returns: Type Description Dict [ T , float ] Precision values at given recall. Source code in great_ai/utilities/evaluate_ranking/evaluate_ranking.py 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 def evaluate_ranking ( expected : List [ T ], actual_scores : List [ float ], target_recall : float , title : Optional [ str ] = \"\" , disable_interpolation : bool = False , axes : Optional [ plt . Axes ] = None , output_svg : Optional [ Path ] = None , reverse_order : bool = False , plot : bool = True , ) -> Dict [ T , float ]: \"\"\"Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's [PR-curve](https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py) Args: expected: Expected ordering of the elements (rank if it's an integer, alphabetical if a string) actual_scores: Actual ranking scores (need not be on the same scale as `expected`) title: Title of the plot. disable_interpolation: Do not interpolate. axes: Matplotlib axes for ploting inside a subplot. output_svg: If specified, save the chart as an svg to the given Path. reverse_order: Reverse the ranking specified by `expected`. plot: Display a plot on the screen. Returns: Precision values at given recall. \"\"\" assert 0 <= target_recall <= 1 if plot and axes is None : fig = plt . figure ( figsize = ( 20 , 10 )) fig . patch . set_facecolor ( \"white\" ) ax = plt . axes () else : ax = axes classes = sorted ( unique ( expected ), reverse = reverse_order ) str_classes = [ str ( c ) for c in classes ] with matplotlib . rc_context ({ \"font.size\" : 20 }): if plot : ax . set_xmargin ( 0 ) draw_f1_iso_lines ( axes = ax ) results : Dict [ T , float ] = {} for i in range ( len ( classes ) - 1 ): binarized_expected = [ ( v < classes [ i ]) if reverse_order else ( v > classes [ i ]) for v in expected ] precision , recall , _ = precision_recall_curve ( binarized_expected , actual_scores ) if not disable_interpolation : for j in range ( 1 , len ( precision )): precision [ j ] = max ( precision [ j - 1 ], precision [ j ]) closest_recall_index = np . argmin ( np . abs ( recall - target_recall )) precision_at_closest_recall = precision [ closest_recall_index ] average_precision = average_precision_score ( binarized_expected , actual_scores ) results [ classes [ i ]] = precision_at_closest_recall if plot : ax . plot ( recall , precision , label = f \" { '|' . join ( str_classes [: i + 1 ]) } \u2194 { '|' . join ( str_classes [ i + 1 :]) } (P@ { target_recall : .2f } = { precision_at_closest_recall : .2f } , AP= { average_precision : .2f } )\" , ) if plot : ax . legend ( loc = \"upper right\" ) ax . axvline ( x = target_recall , linestyle = \"--\" , color = \"#55c6bb\" , linewidth = 2.0 ) if title is None : title = \"Ranking evaluation\" ax . set_title ( f ' { title } ( { \" < \" . join ( str_classes ) } )' , pad = 20 ) ax . set_xlabel ( \"Recall\" ) ax . set_ylabel ( \"Precision\" ) ax . set_xticks ([ target_recall ] + sorted ( ax . get_xticks ())) if plot and output_svg is None : if axes is None : plt . show () elif output_svg : plt . savefig ( output_svg , format = \"svg\" ) return results","title":"evaluate_ranking()"},{"location":"reference/utilities/#parallel-processing","text":"Multiprocessing and multithreading-based parallelism with support for async functions. Its main purpose is to implement great_ai.GreatAI.process_batch , however, the parallel processing functions are also convenient for covering other types of mapping needs with a friendlier API than joblib or multiprocess .","title":"Parallel processing"},{"location":"reference/utilities/#great_ai.utilities.simple_parallel_map","text":"Execute a map operation on an list mimicking the API of the built-in map() . A thin-wrapper over parallel_map . For more options, consult the documentation of parallel_map . Examples: >>> import math >>> list ( simple_parallel_map ( math . sqrt , [ 9 , 4 , 1 ])) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Sequence [ T ] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None Returns: Type Description List [ V ] An iterable of results obtained from applying func to each input value. Raises: Type Description WorkerException If there was an error in the func function in a background process. Source code in great_ai/utilities/parallel_map/simple_parallel_map.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 def simple_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Sequence [ T ], * , chunk_size : Optional [ int ] = None , concurrency : Optional [ int ] = None , ) -> List [ V ]: \"\"\"Execute a map operation on an list mimicking the API of the built-in `map()`. A thin-wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. For more options, consult the documentation of [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. Examples: >>> import math >>> list(simple_parallel_map(math.sqrt, [9, 4, 1])) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. Returns: An iterable of results obtained from applying `func` to each input value. Raises: WorkerException: If there was an error in the `func` function in a background process. \"\"\" input_values = list ( input_values ) # in case the input is mistakenly not a sequence generator = parallel_map ( func = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) return list ( tqdm ( generator , total = len ( input_values ), ) )","title":"simple_parallel_map()"},{"location":"reference/utilities/#great_ai.utilities.parallel_map.parallel_map.parallel_map","text":"Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and async map functions. The func function is serialised with dill . Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some startup cost. Each process processes a single chunk at once. Examples: >>> import math >>> list ( parallel_map ( math . sqrt , [ 9 , 4 , 1 ], concurrency = 2 )) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background process and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/parallel_map.py 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 def parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and `async` map functions. The `func` function is serialised with `dill`. Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some startup cost. Each process processes a single chunk at once. Examples: >>> import math >>> list(parallel_map(math.sqrt, [9, 4, 1], concurrency=2)) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background process and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) ctx = ( mp . get_context ( \"fork\" ) if \"fork\" in mp . get_all_start_methods () else mp . get_context ( \"spawn\" ) ) ctx . freeze_support () manager = ctx . Manager () input_queue = manager . Queue ( config . concurrency * 2 ) output_queue = manager . Queue ( config . concurrency * 2 ) should_stop = ctx . Event () serialized_map_function = dill . dumps ( func , byref = True , recurse = False ) processes = [ ctx . Process ( name = f \"parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = serialized_map_function , ), ) for i in range ( config . concurrency ) ] for p in processes : p . start () try : yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () except WorkerException : should_stop . set () raise except Exception : for p in processes : p . terminate () p . kill () raise finally : for p in processes : p . join () # terminated processes have to be joined else they remain zombies p . close () manager . shutdown ()","title":"parallel_map()"},{"location":"reference/utilities/#great_ai.utilities.threaded_parallel_map","text":"Execute a map operation on an iterable stream. Similar to parallel_map but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and async map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list ( threaded_parallel_map ( lambda x : x ** 2 , [ 1 , 2 , 3 ])) [1, 4, 9] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new threads to start. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background thread and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/threaded_parallel_map.py 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 def threaded_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. Similar to [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and `async` map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list(threaded_parallel_map(lambda x: x ** 2, [1, 2, 3])) [1, 4, 9] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new threads to start. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background thread and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) input_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) output_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) should_stop = threading . Event () threads = [ threading . Thread ( name = f \"threaded_parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = func , ), ) for i in range ( config . concurrency ) ] for t in threads : t . start () yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () for t in threads : t . join ( 1 )","title":"threaded_parallel_map()"},{"location":"reference/utilities/#composable-parallel-processing","text":"Because both threaded_parallel_map and parallel_map have a streaming interface, it is easy to compose them and end up with, for example, a process for each CPU core with its own thread-pool or event-loop. Longer pipelines are also easy to imagine. The chunking methods help in these compositions.","title":"Composable parallel processing"},{"location":"reference/utilities/#great_ai.utilities.chunk.chunk","text":"Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length chunk_size except the last one the length of which is between 1 and chunk_size . Useful for parallel processing. Examples: >>> list ( chunk ( range ( 10 ), chunk_size = 3 )) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Parameters: Name Type Description Default values Iterable [ T ] The stream of items to pack into chunks. required chunk_size int Desired length of each (but the last) chunk. required Yields: Type Description Iterable [ List [ T ]] The next chunk. Source code in great_ai/utilities/chunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def chunk ( values : Iterable [ T ], chunk_size : int ) -> Iterable [ List [ T ]]: \"\"\"Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length `chunk_size` except the last one the length of which is between 1 and `chunk_size`. Useful for parallel processing. Examples: >>> list(chunk(range(10), chunk_size=3)) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Args: values: The stream of items to pack into chunks. chunk_size: Desired length of each (but the last) chunk. Yields: The next chunk. \"\"\" assert chunk_size >= 1 result : List [ T ] = [] for v in values : result . append ( v ) if len ( result ) == chunk_size : yield result result = [] if len ( result ) > 0 : yield result","title":"chunk()"},{"location":"reference/utilities/#great_ai.utilities.unchunk.unchunk","text":"Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of chunk . Useful for parallel processing. Similar to itertools.chain but ignores None chunks. Examples: >>> list ( unchunk ([[ 0 , 1 , 2 ], [ 3 , 4 , 5 ], [ 6 , 7 , 8 ], [ 9 ]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Parameters: Name Type Description Default chunks Iterable [ Optional [ Iterable [ T ]]] Stream of chunks to unpack. required Yields: Type Description Iterable [ T ] The next item in the flattened iterable. Source code in great_ai/utilities/unchunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 def unchunk ( chunks : Iterable [ Optional [ Iterable [ T ]]]) -> Iterable [ T ]: \"\"\"Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of [chunk][great_ai.utilities.chunk.chunk]. Useful for parallel processing. Similar to itertools.chain but ignores `None` chunks. Examples: >>> list(unchunk([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Args: chunks: Stream of chunks to unpack. Yields: The next item in the flattened iterable. \"\"\" for chunk in chunks : if chunk is not None : yield from chunk","title":"unchunk()"},{"location":"reference/utilities/#operations","text":"","title":"Operations"},{"location":"reference/utilities/#great_ai.utilities.ConfigFile","text":"Bases: Mapping [ str , str ] A small and safe INI -style configuration loader with dict and ENV support. The values can be accessed using both dot- and index-notation. It is compatible with the dict interface. File format example: # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV : MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. Examples: >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . zeroth_key 'test' >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . second_key Traceback (most recent call last): ... KeyError : 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile ( 'tests/utilities/data/simple.conf' ) >>> { ** a } {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} Source code in great_ai/utilities/config_file/config_file.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 class ConfigFile ( Mapping [ str , str ]): \"\"\"A small and safe `INI`-style configuration loader with `dict` and `ENV` support. The values can be accessed using both dot- and index-notation. It is compatible with the `dict` interface. File format example: ```toml # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV:MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. ``` Examples: >>> ConfigFile('tests/utilities/data/simple.conf') ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile('tests/utilities/data/simple.conf').zeroth_key 'test' >>> ConfigFile('tests/utilities/data/simple.conf').second_key Traceback (most recent call last): ... KeyError: 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile('tests/utilities/data/simple.conf') >>> {**a} {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} \"\"\" ENVIRONMENT_VARIABLE_KEY_PREFIX = \"ENV\" def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse () @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path def _parse ( self ) -> None : with open ( self . _path , encoding = \"utf-8\" ) as f : lines : str = f . read () matches = pattern . findall ( lines ) for key , * values in matches : try : value = next ( v for v in values if v ) except StopIteration : raise ParseError ( f \"\"\"Cannot parse config file ( { self . _path . absolute () } ), error at key ` { key } `\"\"\" ) already_exists = key in self . _key_values if already_exists and not value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): raise KeyError ( f \"Key ` { key } ` has been already defined and its value is ` { self . _key_values [ key ] } `\" ) if value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): _ , value = value . split ( \":\" ) if value not in os . environ : issue = f \"\"\"The value of ` { key } ` contains the \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } ` prefix but ` { value } ` is not defined as an environment variable\"\"\" if already_exists : logger . warning ( f \"\"\" { issue } , using the default value defined above (` { self . _key_values [ key ] } `)\"\"\" ) continue elif self . _ignore_missing : logger . warning ( issue ) else : raise ValueError ( f \" { issue } and no default value has been provided\" ) else : value = os . environ [ value ] self . _key_values [ key ] = value def __getattr__ ( self , key : str ) -> str : if key in self . _key_values : return self . _key_values [ key ] raise KeyError ( f \"Key ` { key } ` is not found in configuration file ( { self . _path . absolute () } )\" ) __getitem__ = __getattr__ def __iter__ ( self ) -> Iterator [ str ]: return iter ( self . _key_values ) def __len__ ( self ) -> int : return len ( self . _key_values ) def keys ( self ) -> KeysView [ str ]: return self . _key_values . keys () def values ( self ) -> ValuesView [ str ]: return self . _key_values . values () def items ( self ) -> ItemsView [ str , str ]: return self . _key_values . items () def __repr__ ( self ) -> str : return f \" { type ( self ) . __name__ } (path= { self . _path . as_posix () } ) { self . _key_values } \"","title":"ConfigFile"},{"location":"reference/utilities/#great_ai.utilities.config_file.config_file.ConfigFile.__init__","text":"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Parameters: Name Type Description Default path Union [ Path , str ] Local path of the configuration file. required ignore_missing bool Don't raise an exception on missing environment variables. False Raises: Type Description FileNotFoundError If there is no file at the specified path. ParseError If the provided file does not conform to the expected format. KeyError If there is duplication in the keys. ValueError If an environment variable is referenced but it is not set in the system and ignore_missing=False . Source code in great_ai/utilities/config_file/config_file.py 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse ()","title":"__init__()"},{"location":"reference/utilities/#great_ai.utilities.config_file.config_file.ConfigFile.path","text":"Original path from where the configuration was loaded. Source code in great_ai/utilities/config_file/config_file.py 83 84 85 86 @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path","title":"path()"},{"location":"reference/utilities/#great_ai.utilities.get_logger","text":"Return a customised logger used throughout the GreatAI codebase. Uses colors, and only prints timestamps when not running inside notebook. Source code in great_ai/utilities/logger/get_logger.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def get_logger ( name : str , level : int = logging . INFO , disable_colors : bool = False ) -> logging . Logger : \"\"\"Return a customised logger used throughout the GreatAI codebase. Uses colors, and only prints timestamps when not running inside notebook. \"\"\" if name not in loggers : logger = logging . getLogger ( name ) logger . setLevel ( level ) try : get_ipython () # type: ignore log_format = \" %(message)s \" except NameError : # will fail outside of a notebook https://ipython.org/ log_format = \" %(asctime)s | %(levelname)8s | %(message)s \" stdout_handler = logging . StreamHandler () stdout_handler . setLevel ( level ) if not disable_colors : stdout_handler . setFormatter ( CustomFormatter ( log_format )) logger . addHandler ( stdout_handler ) loggers [ name ] = logger return loggers [ name ]","title":"get_logger()"},{"location":"reference/views/","text":"View models # Trace # Bases: Generic [ T ] , HashableBaseModel Source code in great_ai/views/trace.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 class Trace ( Generic [ T ], HashableBaseModel ): trace_id : str created : str original_execution_time_ms : float logged_values : Dict [ str , Any ] models : List [ Model ] exception : Optional [ str ] output : Optional [ T ] feedback : Any = None tags : List [ str ] class Config : extra = Extra . ignore @property def input ( self ) -> Any : return ( self . logged_values [ \"input\" ] if list ( self . logged_values . keys ()) == [ \"input\" ] else self . logged_values ) @property def models_flat ( self ) -> str : return \", \" . join ( f \" { m . key } : { m . version } \" for m in self . models ) @property def output_flat ( self ) -> str : return pformat ( self . output , indent = 2 , compact = True ) @property def exception_flat ( self ) -> str : return ( \"null\" if self . exception is None else pformat ( self . exception , indent = 2 , compact = True ) ) @property def feedback_flat ( self ) -> str : return ( \"null\" if self . feedback is None else pformat ( self . feedback , indent = 2 , compact = True ) ) @property def tags_flat ( self ) -> str : return \", \\n \" . join ( self . tags ) def to_flat_dict ( self , include_original : bool = True ) -> Dict [ str , Any ]: return { ** ( self . dict () if include_original else { \"trace_id\" : self . trace_id , \"created\" : self . created , \"original_execution_time_ms\" : self . original_execution_time_ms , } ), ** { k : pformat ( v , indent = 2 , compact = True ) for k , v in self . logged_values . items () }, \"models_flat\" : self . models_flat , \"exception_flat\" : self . exception_flat , \"output_flat\" : self . output_flat , \"feedback_flat\" : self . feedback_flat , \"tags_flat\" : self . tags_flat , } def __repr__ ( self ) -> str : return f \"\"\"Trace[ { type ( self . output ) . __name__ } ]( { pformat ( self . dict (), indent = 2 , compact = True ) . replace ( '{ ' , '{' , 1 ) } )\"\"\" RouteConfig # Bases: BaseModel Source code in great_ai/views/route_config.py 4 5 6 7 8 9 10 class RouteConfig ( BaseModel ): prediction_endpoint_enabled : bool = True docs_endpoints_enabled : bool = True dashboard_enabled : bool = True feedback_endpoints_enabled : bool = True trace_endpoints_enabled : bool = True meta_endpoints_enabled : bool = True ClassificationOutput # Bases: HashableBaseModel Source code in great_ai/views/outputs/classification_output.py 6 7 8 9 class ClassificationOutput ( HashableBaseModel ): label : Union [ str , int ] confidence : float explanation : Optional [ Any ] MultiLabelClassificationOutput # Bases: HashableBaseModel Source code in great_ai/views/outputs/multi_label_classification_output.py 7 8 class MultiLabelClassificationOutput ( HashableBaseModel ): labels : List [ ClassificationOutput ] = [] RegressionOutput # Bases: HashableBaseModel Source code in great_ai/views/outputs/regression_output.py 6 7 8 class RegressionOutput ( HashableBaseModel ): value : Union [ int , float ] explanation : Optional [ Any ] SequenceLabelingOutput # Bases: HashableBaseModel Source code in great_ai/views/outputs/sequence_labeling_output.py 13 14 15 class SequenceLabelingOutput ( HashableBaseModel ): labeled_tokens : List [ LabeledToken ] explanation : Optional [ Any ]","title":"View models"},{"location":"reference/views/#view-models","text":"","title":"View models"},{"location":"reference/views/#great_ai.Trace","text":"Bases: Generic [ T ] , HashableBaseModel Source code in great_ai/views/trace.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 class Trace ( Generic [ T ], HashableBaseModel ): trace_id : str created : str original_execution_time_ms : float logged_values : Dict [ str , Any ] models : List [ Model ] exception : Optional [ str ] output : Optional [ T ] feedback : Any = None tags : List [ str ] class Config : extra = Extra . ignore @property def input ( self ) -> Any : return ( self . logged_values [ \"input\" ] if list ( self . logged_values . keys ()) == [ \"input\" ] else self . logged_values ) @property def models_flat ( self ) -> str : return \", \" . join ( f \" { m . key } : { m . version } \" for m in self . models ) @property def output_flat ( self ) -> str : return pformat ( self . output , indent = 2 , compact = True ) @property def exception_flat ( self ) -> str : return ( \"null\" if self . exception is None else pformat ( self . exception , indent = 2 , compact = True ) ) @property def feedback_flat ( self ) -> str : return ( \"null\" if self . feedback is None else pformat ( self . feedback , indent = 2 , compact = True ) ) @property def tags_flat ( self ) -> str : return \", \\n \" . join ( self . tags ) def to_flat_dict ( self , include_original : bool = True ) -> Dict [ str , Any ]: return { ** ( self . dict () if include_original else { \"trace_id\" : self . trace_id , \"created\" : self . created , \"original_execution_time_ms\" : self . original_execution_time_ms , } ), ** { k : pformat ( v , indent = 2 , compact = True ) for k , v in self . logged_values . items () }, \"models_flat\" : self . models_flat , \"exception_flat\" : self . exception_flat , \"output_flat\" : self . output_flat , \"feedback_flat\" : self . feedback_flat , \"tags_flat\" : self . tags_flat , } def __repr__ ( self ) -> str : return f \"\"\"Trace[ { type ( self . output ) . __name__ } ]( { pformat ( self . dict (), indent = 2 , compact = True ) . replace ( '{ ' , '{' , 1 ) } )\"\"\"","title":"Trace"},{"location":"reference/views/#great_ai.RouteConfig","text":"Bases: BaseModel Source code in great_ai/views/route_config.py 4 5 6 7 8 9 10 class RouteConfig ( BaseModel ): prediction_endpoint_enabled : bool = True docs_endpoints_enabled : bool = True dashboard_enabled : bool = True feedback_endpoints_enabled : bool = True trace_endpoints_enabled : bool = True meta_endpoints_enabled : bool = True","title":"RouteConfig"},{"location":"reference/views/#great_ai.ClassificationOutput","text":"Bases: HashableBaseModel Source code in great_ai/views/outputs/classification_output.py 6 7 8 9 class ClassificationOutput ( HashableBaseModel ): label : Union [ str , int ] confidence : float explanation : Optional [ Any ]","title":"ClassificationOutput"},{"location":"reference/views/#great_ai.MultiLabelClassificationOutput","text":"Bases: HashableBaseModel Source code in great_ai/views/outputs/multi_label_classification_output.py 7 8 class MultiLabelClassificationOutput ( HashableBaseModel ): labels : List [ ClassificationOutput ] = []","title":"MultiLabelClassificationOutput"},{"location":"reference/views/#great_ai.RegressionOutput","text":"Bases: HashableBaseModel Source code in great_ai/views/outputs/regression_output.py 6 7 8 class RegressionOutput ( HashableBaseModel ): value : Union [ int , float ] explanation : Optional [ Any ]","title":"RegressionOutput"},{"location":"reference/views/#great_ai.SequenceLabelingOutput","text":"Bases: HashableBaseModel Source code in great_ai/views/outputs/sequence_labeling_output.py 13 14 15 class SequenceLabelingOutput ( HashableBaseModel ): labeled_tokens : List [ LabeledToken ] explanation : Optional [ Any ]","title":"SequenceLabelingOutput"},{"location":"tutorial/","text":"Train and deploy a SOTA model # Let's see great-ai in action by going over the life-cycle of a simple service. Objectives # You will see how the great_ai.utilities can integrate into your Data Science workflow. You will use great_ai.large_file to version and store your trained model. You will use GreatAI to prepare your model for a robust and responsible deployment. Overview # You are going to train a field of study (domain) classifier for scientific sentences. The exact task was proposed by the SciBERT paper in which SciBERT achieved an F1-score of 0.6571 . We are going to outperform it using a trivial text classification model: a Linear SVM . We use the same synthetic dataset derived from the Microsoft Academic Graph . The dataset is available here . Success You are ready to start the tutorial. Feel free to come back to the summary section once you're finished. Train it Deploy it Summary # Training notebook # We load and preprocess the dataset while relying on great_ai.utilities.clean for doing the heavy-lifting. Additionally, the preprocessing is parallelised using great_ai.utilities.simple_parallel_map After training and evaluating a model, it is exported using great_ai.save_model . Remote storage To store your model remotely, you need to set your credentials before calling save_model . For example, to use AWS S3 : from great_ai.large_file import LargeFileS3 LargeFileS3 . configure ( aws_region_name = 'eu-west-2' , aws_access_key_id = 'MY_AWS_ACCESS_KEY' , aws_secret_access_key = 'MY_AWS_SECRET_KEY' , large_files_bucket_name = 'my_bucket_for_models' ) from great_ai import save_model save_model ( model , key = 'my-domain-predictor' ) For more info, checkout the configuration how-to page . Deployment notebook # We create an inference function that can be hardened by wrapping it in a GreatAI instance. from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( 'my-domain-predictor' ) #(1) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) @use_model loads and injects your model into the predict_domain function's model argument. You can freely reference it knowing that the function is always provided with it. Finally, we test the model's inference function through the GreatAI dashboard. The only thing left is to deploy the hardened service properly. Learn about all the features Look at more examples","title":"Tutorial overview"},{"location":"tutorial/#train-and-deploy-a-sota-model","text":"Let's see great-ai in action by going over the life-cycle of a simple service.","title":"Train and deploy a SOTA model"},{"location":"tutorial/#objectives","text":"You will see how the great_ai.utilities can integrate into your Data Science workflow. You will use great_ai.large_file to version and store your trained model. You will use GreatAI to prepare your model for a robust and responsible deployment.","title":"Objectives"},{"location":"tutorial/#overview","text":"You are going to train a field of study (domain) classifier for scientific sentences. The exact task was proposed by the SciBERT paper in which SciBERT achieved an F1-score of 0.6571 . We are going to outperform it using a trivial text classification model: a Linear SVM . We use the same synthetic dataset derived from the Microsoft Academic Graph . The dataset is available here . Success You are ready to start the tutorial. Feel free to come back to the summary section once you're finished. Train it Deploy it","title":"Overview"},{"location":"tutorial/#summary","text":"","title":"Summary"},{"location":"tutorial/#training-notebook","text":"We load and preprocess the dataset while relying on great_ai.utilities.clean for doing the heavy-lifting. Additionally, the preprocessing is parallelised using great_ai.utilities.simple_parallel_map After training and evaluating a model, it is exported using great_ai.save_model . Remote storage To store your model remotely, you need to set your credentials before calling save_model . For example, to use AWS S3 : from great_ai.large_file import LargeFileS3 LargeFileS3 . configure ( aws_region_name = 'eu-west-2' , aws_access_key_id = 'MY_AWS_ACCESS_KEY' , aws_secret_access_key = 'MY_AWS_SECRET_KEY' , large_files_bucket_name = 'my_bucket_for_models' ) from great_ai import save_model save_model ( model , key = 'my-domain-predictor' ) For more info, checkout the configuration how-to page .","title":"Training notebook"},{"location":"tutorial/#deployment-notebook","text":"We create an inference function that can be hardened by wrapping it in a GreatAI instance. from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( 'my-domain-predictor' ) #(1) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) @use_model loads and injects your model into the predict_domain function's model argument. You can freely reference it knowing that the function is always provided with it. Finally, we test the model's inference function through the GreatAI dashboard. The only thing left is to deploy the hardened service properly. Learn about all the features Look at more examples","title":"Deployment notebook"},{"location":"tutorial/deploy/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); const range = document.createRange(); range.selectNodeContents(node); selection.addRange(range); document.execCommand('copy'); selection.removeAllRanges(); return Promise.resolve(); } function copyText(text) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(text); } const body = document.body; if (!body) { return Promise.reject(new Error()); } const node = createNode(text); body.appendChild(node); copyNode(node); body.removeChild(node); return Promise.resolve(); } function copy(button) { const id = button.getAttribute('for'); const text = button.getAttribute('value'); function trigger() { button.dispatchEvent(new CustomEvent('clipboard-copy', { bubbles: true })); } if (text) { copyText(text).then(trigger); } else if (id) { const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument; if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return; const node = root.getElementById(id); if (node) copyTarget(node).then(trigger); } } function copyTarget(content) { if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) { return copyText(content.value); } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) { return copyText(content.href); } else { return copyNode(content); } } function clicked(event) { const button = event.currentTarget; if (button instanceof HTMLElement) { copy(button); } } function keydown(event) { if (event.key === ' ' || event.key === 'Enter') { const button = event.currentTarget; if (button instanceof HTMLElement) { event.preventDefault(); copy(button); } } } function focused(event) { event.currentTarget.addEventListener('keydown', keydown); } function blurred(event) { event.currentTarget.removeEventListener('keydown', keydown); } class ClipboardCopyElement extends HTMLElement { constructor() { super(); this.addEventListener('click', clicked); this.addEventListener('focus', focused); this.addEventListener('blur', blurred); } connectedCallback() { if (!this.hasAttribute('tabindex')) { this.setAttribute('tabindex', '0'); } if (!this.hasAttribute('role')) { this.setAttribute('role', 'button'); } } get value() { return this.getAttribute('value') || ''; } set value(text) { this.setAttribute('value', text); } } if (!window.customElements.get('clipboard-copy')) { window.ClipboardCopyElement = ClipboardCopyElement; window.customElements.define('clipboard-copy', ClipboardCopyElement); } return ClipboardCopyElement; })); document.addEventListener('clipboard-copy', function(event) { const notice = event.target.querySelector('.notice') notice.hidden = false setTimeout(function() { notice.hidden = true }, 1000) }) pre { line-height: 125%; } td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Harden and deploy your app \u00b6 Finally, it's time to deploy your model. But before that, you have to make sure you follow AI deployment best-practices . In the past, this step was too often either the source of unexpected struggle, or worse, simply ignored. With GreatAI , it has become a matter of 4 lines of code. In [1]: Copied! from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( \"my-domain-predictor\" ) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) from great_ai import GreatAI, use_model from great_ai.utilities import clean @GreatAI.create @use_model(\"my-domain-predictor\") def predict_domain(sentence, model): inputs = [clean(sentence)] return str(model.predict(inputs)[0]) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.4): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of my-domain-predictor Latest version of my-domain-predictor is 9 (from versions: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9) File my-domain-predictor-9 found in cache In [2]: Copied! predict_domain ( \"Mountains are just big rocks.\" ) predict_domain(\"Mountains are just big rocks.\") Out[2]: Trace[str]({'created': '2022-07-12T13:34:26.743292', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 29, 'arg:sentence:value': 'Mountains are just big rocks.'}, 'models': [{'key': 'my-domain-predictor', 'version': 9}], 'original_execution_time_ms': 6.9699, 'output': 'geography', 'tags': ['predict_domain', 'online', 'development'], 'trace_id': 'c80bdee3-602b-49dd-a84d-6eef80127e5a'}) Notice how the original return value is under the .output key. Additionally, a plethora of metadata has been added which will be useful later on. Running your app in development-mode is as easy as executing great-ai deploy.ipynb from your terminal. In [3]: Copied! ! great - ai deploy . ipynb # leave this running and open http://127.0.0.1:6060 !great-ai deploy.ipynb # leave this running and open http://127.0.0.1:6060 2022-07-12 15:34:28 | INFO | Converting notebook to Python script 2022-07-12 15:34:29 | INFO | Found `predict_domain` to be the GreatAI app 2022-07-12 15:34:29 | INFO | Uvicorn running on http://0.0.0.0:6060 (Press CTRL+C to quit) 2022-07-12 15:34:31 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-07-12 15:34:31 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver 2022-07-12 15:34:31 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production 2022-07-12 15:34:31 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal 2022-07-12 15:34:31 | INFO | GreatAI (v0.1.4): configured \u2705 2022-07-12 15:34:31 | INFO | \ud83d\udd29 tracing_database: ParallelTinyDbDriver 2022-07-12 15:34:31 | INFO | \ud83d\udd29 large_file_implementation: LargeFileLocal 2022-07-12 15:34:31 | INFO | \ud83d\udd29 is_production: False 2022-07-12 15:34:31 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-07-12 15:34:31 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-07-12 15:34:31 | INFO | \ud83d\udd29 dashboard_table_size: 50 2022-07-12 15:34:31 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-07-12 15:34:31 | WARNING | > Find out more at https://se-ml.github.io/practices 2022-07-12 15:34:31 | INFO | Fetching cached versions of my-domain-predictor 2022-07-12 15:34:31 | INFO | Latest version of my-domain-predictor is 9 (from versions: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9) 2022-07-12 15:34:31 | INFO | File my-domain-predictor-9 found in cache 2022-07-12 15:34:31 | INFO | Started server process [199794] 2022-07-12 15:34:31 | INFO | Waiting for application startup. 2022-07-12 15:34:31 | INFO | Application startup complete. ^C 2022-07-12 15:34:33 | INFO | Shutting down 2022-07-12 15:34:33 | INFO | Waiting for application shutdown. 2022-07-12 15:34:33 | INFO | Application shutdown complete. 2022-07-12 15:34:33 | INFO | Finished server process [199794] Congrats, you've just created your first GreatAI service! \ud83c\udf89 Now that you've made sure your application is hardened enough for the intended use case, it is time to deploy it. The responsibilities of GreatAI end when it wraps your inference function and model into a production-ready service. You're given the freedom and responsibility to deploy this service. Fortunately, you (or your organisation) probably already have an established routine for deploying services. There are three main approaches to deploy a GreatAI service: For more info about them, check out the deployment how-to . For more thorough examples, see the examples page . Go back to the summary \u00b6","title":"Harden and deploy your app"},{"location":"tutorial/deploy/#harden-and-deploy-your-app","text":"Finally, it's time to deploy your model. But before that, you have to make sure you follow AI deployment best-practices . In the past, this step was too often either the source of unexpected struggle, or worse, simply ignored. With GreatAI , it has become a matter of 4 lines of code. In [1]: Copied! from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( \"my-domain-predictor\" ) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) from great_ai import GreatAI, use_model from great_ai.utilities import clean @GreatAI.create @use_model(\"my-domain-predictor\") def predict_domain(sentence, model): inputs = [clean(sentence)] return str(model.predict(inputs)[0]) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.4): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of my-domain-predictor Latest version of my-domain-predictor is 9 (from versions: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9) File my-domain-predictor-9 found in cache In [2]: Copied! predict_domain ( \"Mountains are just big rocks.\" ) predict_domain(\"Mountains are just big rocks.\") Out[2]: Trace[str]({'created': '2022-07-12T13:34:26.743292', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 29, 'arg:sentence:value': 'Mountains are just big rocks.'}, 'models': [{'key': 'my-domain-predictor', 'version': 9}], 'original_execution_time_ms': 6.9699, 'output': 'geography', 'tags': ['predict_domain', 'online', 'development'], 'trace_id': 'c80bdee3-602b-49dd-a84d-6eef80127e5a'}) Notice how the original return value is under the .output key. Additionally, a plethora of metadata has been added which will be useful later on. Running your app in development-mode is as easy as executing great-ai deploy.ipynb from your terminal. In [3]: Copied! ! great - ai deploy . ipynb # leave this running and open http://127.0.0.1:6060 !great-ai deploy.ipynb # leave this running and open http://127.0.0.1:6060 2022-07-12 15:34:28 | INFO | Converting notebook to Python script 2022-07-12 15:34:29 | INFO | Found `predict_domain` to be the GreatAI app 2022-07-12 15:34:29 | INFO | Uvicorn running on http://0.0.0.0:6060 (Press CTRL+C to quit) 2022-07-12 15:34:31 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-07-12 15:34:31 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver 2022-07-12 15:34:31 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production 2022-07-12 15:34:31 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal 2022-07-12 15:34:31 | INFO | GreatAI (v0.1.4): configured \u2705 2022-07-12 15:34:31 | INFO | \ud83d\udd29 tracing_database: ParallelTinyDbDriver 2022-07-12 15:34:31 | INFO | \ud83d\udd29 large_file_implementation: LargeFileLocal 2022-07-12 15:34:31 | INFO | \ud83d\udd29 is_production: False 2022-07-12 15:34:31 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-07-12 15:34:31 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-07-12 15:34:31 | INFO | \ud83d\udd29 dashboard_table_size: 50 2022-07-12 15:34:31 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-07-12 15:34:31 | WARNING | > Find out more at https://se-ml.github.io/practices 2022-07-12 15:34:31 | INFO | Fetching cached versions of my-domain-predictor 2022-07-12 15:34:31 | INFO | Latest version of my-domain-predictor is 9 (from versions: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9) 2022-07-12 15:34:31 | INFO | File my-domain-predictor-9 found in cache 2022-07-12 15:34:31 | INFO | Started server process [199794] 2022-07-12 15:34:31 | INFO | Waiting for application startup. 2022-07-12 15:34:31 | INFO | Application startup complete. ^C 2022-07-12 15:34:33 | INFO | Shutting down 2022-07-12 15:34:33 | INFO | Waiting for application shutdown. 2022-07-12 15:34:33 | INFO | Application shutdown complete. 2022-07-12 15:34:33 | INFO | Finished server process [199794] Congrats, you've just created your first GreatAI service! \ud83c\udf89 Now that you've made sure your application is hardened enough for the intended use case, it is time to deploy it. The responsibilities of GreatAI end when it wraps your inference function and model into a production-ready service. You're given the freedom and responsibility to deploy this service. Fortunately, you (or your organisation) probably already have an established routine for deploying services. There are three main approaches to deploy a GreatAI service: For more info about them, check out the deployment how-to . For more thorough examples, see the examples page .","title":"Harden and deploy your app"},{"location":"tutorial/deploy/#go-back-to-the-summary","text":"","title":"Go back to the summary"},{"location":"tutorial/train/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Train your model \u00b6 The first step is, of course, to install great-ai in your Python environment. In [1]: Copied! % pip install great - ai > / dev / null %pip install great-ai > /dev/null Note: you may need to restart the kernel to use updated packages. First, we have to get some data. After downloading it from here , we might notice that the dataset is in JSON Lines format (each line is a separate JSON document). Let's write a function which takes a single line and returns the sentence and the corresponding label from it. Before returning, the sentence is also cleaned to remove any LaTeX, XML, unicode, PDF-extraction artifacts. In [2]: Copied! import json from great_ai.utilities import clean def preprocess ( line ): data_point = json . loads ( line ) sentence = data_point [ \"text\" ] label = data_point [ \"label\" ] return clean ( sentence ), label import json from great_ai.utilities import clean def preprocess(line): data_point = json.loads(line) sentence = data_point[\"text\"] label = data_point[\"label\"] return clean(sentence), label Now, we can load the dataset and extract the training samples from it. Since we're impatient, we can do it in parallel using the simple_parallel_map function. Open files in Python are iterable: in text mode, each iteration returns the next line. In [3]: Copied! from great_ai.utilities import simple_parallel_map with open ( \"data/train.txt\" , encoding = \"utf-8\" ) as f : training_data = simple_parallel_map ( preprocess , f ) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] from great_ai.utilities import simple_parallel_map with open(\"data/train.txt\", encoding=\"utf-8\") as f: training_data = simple_parallel_map(preprocess, f) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84000/84000 [00:09<00:00, 8960.63it/s] Let's do the same for the test data. In [4]: Copied! with open ( \"data/test.txt\" , encoding = \"utf-8\" ) as f : test_data = simple_parallel_map ( preprocess , f ) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] with open(\"data/test.txt\", encoding=\"utf-8\") as f: test_data = simple_parallel_map(preprocess, f) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22399/22399 [00:03<00:00, 6078.02it/s] After obtaining some clean data, it's time to create and train a model on it. We are going to use scikit-learn for this purpose. In this case, we opted for a linear Support Vector Machine because it's known to be an adequate baseline for simple classification tasks. In [5]: Copied! from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline ( TfidfVectorizer (), LinearSVC ()) # todo: hyperparameter-optimisation model . fit ( X_train , y_train ) from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline(TfidfVectorizer(), LinearSVC()) # todo: hyperparameter-optimisation model.fit(X_train, y_train) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) TfidfVectorizer TfidfVectorizer() LinearSVC LinearSVC() It's already finished. Let's see how well it performs. But first, we need to configure matplotlib to make the charts more legible. ConfusionMatrixDisplay relies on matplotlib for rendering. In [6]: Copied! import matplotlib.pyplot as plt % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 import matplotlib.pyplot as plt %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 Next, we check the quality of the model on the test split. We can use the classification report to check the common metrics, such as the macro F1-score. We also draw the confusion matrix to get some insights into the types of mistakes being made by the model. In [7]: Copied! from sklearn import metrics y_predicted = model . predict ( X_test ) print ( metrics . classification_report ( y_test , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_test , y_predicted ) None from sklearn import metrics y_predicted = model.predict(X_test) print(metrics.classification_report(y_test, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions(y_test, y_predicted) None precision recall f1-score support business 0.67 0.69 0.68 3198 economics 0.69 0.71 0.70 3189 geography 0.70 0.73 0.72 3207 medicine 0.90 0.91 0.90 3187 politics 0.63 0.57 0.60 3169 psychology 0.73 0.73 0.73 3252 sociology 0.51 0.51 0.51 3197 accuracy 0.69 22399 macro avg 0.69 0.69 0.69 22399 weighted avg 0.69 0.69 0.69 22399 Great work, we can be rightfully satisfied with our model. Seeing the results, we achieved an F1-score of 0.69 which is about 5% better than SciBERT's 0.6571! You might wonder that \"this is great, but besides some utility functions ( clean , simple_parallel_map , ...) what more value does GreatAI add?\" . This would be a valid argument because the scope of GreatAI actually only starts here. Not coincidentally, this is the point where the scope of Data Science ends but it's still a grey zone for software engineering. In order to use this model in production, we have to make it available on some possibly shared infrastructure. In [8]: Copied! from great_ai import save_model save_model ( model , key = \"my-domain-predictor\" ) from great_ai import save_model save_model(model, key=\"my-domain-predictor\") Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.4): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of my-domain-predictor Copying file for my-domain-predictor-9 Compressing my-domain-predictor-9 Model my-domain-predictor uploaded with version 9 Out[8]: 'my-domain-predictor:9' Let's continue by finishing the deployment in the next notebook. \u00b6","title":"Train your model"},{"location":"tutorial/train/#train-your-model","text":"The first step is, of course, to install great-ai in your Python environment. In [1]: Copied! % pip install great - ai > / dev / null %pip install great-ai > /dev/null Note: you may need to restart the kernel to use updated packages. First, we have to get some data. After downloading it from here , we might notice that the dataset is in JSON Lines format (each line is a separate JSON document). Let's write a function which takes a single line and returns the sentence and the corresponding label from it. Before returning, the sentence is also cleaned to remove any LaTeX, XML, unicode, PDF-extraction artifacts. In [2]: Copied! import json from great_ai.utilities import clean def preprocess ( line ): data_point = json . loads ( line ) sentence = data_point [ \"text\" ] label = data_point [ \"label\" ] return clean ( sentence ), label import json from great_ai.utilities import clean def preprocess(line): data_point = json.loads(line) sentence = data_point[\"text\"] label = data_point[\"label\"] return clean(sentence), label Now, we can load the dataset and extract the training samples from it. Since we're impatient, we can do it in parallel using the simple_parallel_map function. Open files in Python are iterable: in text mode, each iteration returns the next line. In [3]: Copied! from great_ai.utilities import simple_parallel_map with open ( \"data/train.txt\" , encoding = \"utf-8\" ) as f : training_data = simple_parallel_map ( preprocess , f ) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] from great_ai.utilities import simple_parallel_map with open(\"data/train.txt\", encoding=\"utf-8\") as f: training_data = simple_parallel_map(preprocess, f) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84000/84000 [00:09<00:00, 8960.63it/s] Let's do the same for the test data. In [4]: Copied! with open ( \"data/test.txt\" , encoding = \"utf-8\" ) as f : test_data = simple_parallel_map ( preprocess , f ) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] with open(\"data/test.txt\", encoding=\"utf-8\") as f: test_data = simple_parallel_map(preprocess, f) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22399/22399 [00:03<00:00, 6078.02it/s] After obtaining some clean data, it's time to create and train a model on it. We are going to use scikit-learn for this purpose. In this case, we opted for a linear Support Vector Machine because it's known to be an adequate baseline for simple classification tasks. In [5]: Copied! from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline ( TfidfVectorizer (), LinearSVC ()) # todo: hyperparameter-optimisation model . fit ( X_train , y_train ) from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline(TfidfVectorizer(), LinearSVC()) # todo: hyperparameter-optimisation model.fit(X_train, y_train) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) TfidfVectorizer TfidfVectorizer() LinearSVC LinearSVC() It's already finished. Let's see how well it performs. But first, we need to configure matplotlib to make the charts more legible. ConfusionMatrixDisplay relies on matplotlib for rendering. In [6]: Copied! import matplotlib.pyplot as plt % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 import matplotlib.pyplot as plt %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 Next, we check the quality of the model on the test split. We can use the classification report to check the common metrics, such as the macro F1-score. We also draw the confusion matrix to get some insights into the types of mistakes being made by the model. In [7]: Copied! from sklearn import metrics y_predicted = model . predict ( X_test ) print ( metrics . classification_report ( y_test , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_test , y_predicted ) None from sklearn import metrics y_predicted = model.predict(X_test) print(metrics.classification_report(y_test, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions(y_test, y_predicted) None precision recall f1-score support business 0.67 0.69 0.68 3198 economics 0.69 0.71 0.70 3189 geography 0.70 0.73 0.72 3207 medicine 0.90 0.91 0.90 3187 politics 0.63 0.57 0.60 3169 psychology 0.73 0.73 0.73 3252 sociology 0.51 0.51 0.51 3197 accuracy 0.69 22399 macro avg 0.69 0.69 0.69 22399 weighted avg 0.69 0.69 0.69 22399 Great work, we can be rightfully satisfied with our model. Seeing the results, we achieved an F1-score of 0.69 which is about 5% better than SciBERT's 0.6571! You might wonder that \"this is great, but besides some utility functions ( clean , simple_parallel_map , ...) what more value does GreatAI add?\" . This would be a valid argument because the scope of GreatAI actually only starts here. Not coincidentally, this is the point where the scope of Data Science ends but it's still a grey zone for software engineering. In order to use this model in production, we have to make it available on some possibly shared infrastructure. In [8]: Copied! from great_ai import save_model save_model ( model , key = \"my-domain-predictor\" ) from great_ai import save_model save_model(model, key=\"my-domain-predictor\") Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.4): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of my-domain-predictor Copying file for my-domain-predictor-9 Compressing my-domain-predictor-9 Model my-domain-predictor uploaded with version 9 Out[8]: 'my-domain-predictor:9'","title":"Train your model"},{"location":"tutorial/train/#lets-continue-by-finishing-the-deployment-in-the-next-notebook","text":"","title":"Let's continue by finishing the deployment in the next notebook."}]}
\ No newline at end of file
+{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"Overview of GreatAI Applying AI is becoming increasingly easier but many case studies have shown that these applications are often deployed poorly. This may lead to suboptimal performance and to introducing unintended biases . GreatAI helps fixing this by allowing you to easily transform your prototype AI code into production-ready software . Case studies \"There is a need to consider and adapt well established SE practices which have been ignored or had a very narrow focus in ML literature.\" \u2014 John et al. \"Finally, we have found that existing tools to aid Machine Learning development do not address the specificities of different projects, and thus, are seldom adopted by teams.\" \u2014 Haakman et al. \"Because a mature system might end up being (at most) 5% machine learning code and (at least) 95% glue code, it may be less costly to create a clean native solution rather than re-use a generic package.\" \u2014 Sculley et al. \"For example, practice 25 is very important for \u201cTraceability\", yet relatively weakly adopted. We expect that the results from this type of analysis can, in the future, provide useful guidance for practitioners in terms of aiding them to assess their rate of adoption for each practice and to create roadmaps for improving their processes. \u2014 Serban et al. Features # Save prediction traces of each prediction including arguments and model versions Save feedback and merge it into a ground-truth database Version and store models and data on shared infrastructure (MongoDB GridFS, S3-compatible storage, shared local-volume) Automatically scaffolded custom REST API (and OpenAPI schema) for easy integration Input validation Sensible cache-policy Seamless support for both synchronous and asynchronous inference methods Easy integration with remote GreatAI instances Built-in parallelisation (with support for multiprocessing, async, and mixed modes) for batch processing Well-tested utilities for common NLP tasks (cleaning, language-tagging, sentence-segmentation, etc.) A simple, unified configuration interface Fully-typed API for Pylance and MyPy support Auto-reload for development Docker support for deployment Deployable Jupyter Notebooks Dashboard for high-level overview and analysing traces Roadmap # Prometheus & Grafana integration Well-tested feature extraction code for non-NLP data Support for direct file input Support for PostgreSQL Hello world # pip install great-ai demo.py from great_ai import GreatAI @GreatAI . create #(1) def greeter ( name : str ) -> str : #(2) return f \"Hello { name } !\" @GreatAI.create wraps your greeter function with a GreatAI instance. The function will behave very similarly but: its return value becomes a Trace[str] , it gets a process_batch method for supporting parallel execution, and it can be deployed using the great-ai command-line tool. Typing functions is recommended in general, however, not required for GreatAI to work. Note In practice, greeter could be an inference function of some AI/ML application. But it could also just wrap a black-box solution of some SaaS. Either way, it is imperative to have continuous oversight of the services you provide and data you process especially in the context of AI/ML applications. terminal great-ai demo.py Navigate to localhost:6060 in your browser. Success Your GreatAI service is ready for production use. Many of the SE4ML best-practices are configured and implemented automatically. To have full control over your service and to understand what else you might need to do in your use case, continue reading this documentation. Why is this GREAT? # GreatAI fits between the prototype and deployment phases of your (or your organisation's) AI development lifecycle. This is highlighted with blue in the diagram. Here, several best practices can be automatically implemented aiming to achieve the following attributes: G eneral: use any Python library without restriction R obust: have error-handling and well-tested utilities out-of-the-box E nd-to-end: utilise end-to-end feedback as a built-in, first-class concept A utomated: focus only on what actually requires your attention T rustworthy: deploy models that you and society can confidently trust Why GreatAI? # There are other, existing solutions aiming to facilitate this phase. Amazon SageMaker and Seldon Core provide the most comprehensive suite of features. If you have the opportunity to use them, do that because they're great. However, research indicates that professionals rarely use them. This may be due to their inherent setup and operating complexity. GreatAI is designed to be as simple to use as possible. Its clear, high-level API and sensible default configuration makes it extremely easy to start using. Despite its relative simplicity over Seldon Core, it still implements many of the SE4ML best-practices , and thus, can meaningfully improve your deployment without requiring prohibitively large effort. Find it on PyPI Find it on DockerHub Check out the tutorial Production use # GreatAI has been battle-tested on the core platform services of ScoutinScience .","title":"Overview"},{"location":"#features","text":"Save prediction traces of each prediction including arguments and model versions Save feedback and merge it into a ground-truth database Version and store models and data on shared infrastructure (MongoDB GridFS, S3-compatible storage, shared local-volume) Automatically scaffolded custom REST API (and OpenAPI schema) for easy integration Input validation Sensible cache-policy Seamless support for both synchronous and asynchronous inference methods Easy integration with remote GreatAI instances Built-in parallelisation (with support for multiprocessing, async, and mixed modes) for batch processing Well-tested utilities for common NLP tasks (cleaning, language-tagging, sentence-segmentation, etc.) A simple, unified configuration interface Fully-typed API for Pylance and MyPy support Auto-reload for development Docker support for deployment Deployable Jupyter Notebooks Dashboard for high-level overview and analysing traces","title":"Features"},{"location":"#roadmap","text":"Prometheus & Grafana integration Well-tested feature extraction code for non-NLP data Support for direct file input Support for PostgreSQL","title":"Roadmap"},{"location":"#hello-world","text":"pip install great-ai demo.py from great_ai import GreatAI @GreatAI . create #(1) def greeter ( name : str ) -> str : #(2) return f \"Hello { name } !\" @GreatAI.create wraps your greeter function with a GreatAI instance. The function will behave very similarly but: its return value becomes a Trace[str] , it gets a process_batch method for supporting parallel execution, and it can be deployed using the great-ai command-line tool. Typing functions is recommended in general, however, not required for GreatAI to work. Note In practice, greeter could be an inference function of some AI/ML application. But it could also just wrap a black-box solution of some SaaS. Either way, it is imperative to have continuous oversight of the services you provide and data you process especially in the context of AI/ML applications. terminal great-ai demo.py Navigate to localhost:6060 in your browser. Success Your GreatAI service is ready for production use. Many of the SE4ML best-practices are configured and implemented automatically. To have full control over your service and to understand what else you might need to do in your use case, continue reading this documentation.","title":"Hello world"},{"location":"#why-is-this-great","text":"GreatAI fits between the prototype and deployment phases of your (or your organisation's) AI development lifecycle. This is highlighted with blue in the diagram. Here, several best practices can be automatically implemented aiming to achieve the following attributes: G eneral: use any Python library without restriction R obust: have error-handling and well-tested utilities out-of-the-box E nd-to-end: utilise end-to-end feedback as a built-in, first-class concept A utomated: focus only on what actually requires your attention T rustworthy: deploy models that you and society can confidently trust","title":"Why is this GREAT?"},{"location":"#why-greatai","text":"There are other, existing solutions aiming to facilitate this phase. Amazon SageMaker and Seldon Core provide the most comprehensive suite of features. If you have the opportunity to use them, do that because they're great. However, research indicates that professionals rarely use them. This may be due to their inherent setup and operating complexity. GreatAI is designed to be as simple to use as possible. Its clear, high-level API and sensible default configuration makes it extremely easy to start using. Despite its relative simplicity over Seldon Core, it still implements many of the SE4ML best-practices , and thus, can meaningfully improve your deployment without requiring prohibitively large effort. Find it on PyPI Find it on DockerHub Check out the tutorial","title":"Why GreatAI?"},{"location":"#production-use","text":"GreatAI has been battle-tested on the core platform services of ScoutinScience .","title":"Production use"},{"location":"explanation/","text":"Explanation # A lot more details and discussion about the problem context and approaches of GreatAI along with its evaluation can be found in my thesis. : Download (available soon)","title":"Explanation"},{"location":"explanation/#explanation","text":"A lot more details and discussion about the problem context and approaches of GreatAI along with its evaluation can be found in my thesis. : Download (available soon)","title":"Explanation"},{"location":"examples/scibert/","text":"Summarising scientific publications from a tech-transfer perspective # This example shows how great-ai is used in practice at ScoutinScience. Check out the code","title":"Explainable SciBERT"},{"location":"examples/scibert/#summarising-scientific-publications-from-a-tech-transfer-perspective","text":"This example shows how great-ai is used in practice at ScoutinScience. Check out the code","title":"Summarising scientific publications from a tech-transfer perspective"},{"location":"examples/scibert/views/__init__/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); 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In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: 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var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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selection.addRange(range); document.execCommand('copy'); selection.removeAllRanges(); return Promise.resolve(); } function copyText(text) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(text); } const body = document.body; if (!body) { return Promise.reject(new Error()); } const node = createNode(text); body.appendChild(node); copyNode(node); body.removeChild(node); return Promise.resolve(); } function copy(button) { const id = button.getAttribute('for'); const text = button.getAttribute('value'); function trigger() { button.dispatchEvent(new CustomEvent('clipboard-copy', { bubbles: true })); } if (text) { copyText(text).then(trigger); } else if (id) { const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument; if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return; const node = root.getElementById(id); if (node) copyTarget(node).then(trigger); } } function copyTarget(content) { if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) { return copyText(content.value); } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) { return copyText(content.href); } else { return copyNode(content); } } function clicked(event) { const button = event.currentTarget; if (button instanceof HTMLElement) { copy(button); } } function keydown(event) { if (event.key === ' ' || event.key === 'Enter') { const button = event.currentTarget; if (button instanceof HTMLElement) { event.preventDefault(); copy(button); } } } function focused(event) { event.currentTarget.addEventListener('keydown', keydown); } function blurred(event) { event.currentTarget.removeEventListener('keydown', keydown); } class ClipboardCopyElement extends HTMLElement { constructor() { super(); this.addEventListener('click', clicked); 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} /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); const range = document.createRange(); range.selectNodeContents(node); selection.addRange(range); document.execCommand('copy'); selection.removeAllRanges(); return Promise.resolve(); } function copyText(text) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(text); } const body = document.body; if (!body) { return Promise.reject(new Error()); } const node = createNode(text); body.appendChild(node); copyNode(node); body.removeChild(node); return Promise.resolve(); } function copy(button) { const id = button.getAttribute('for'); const text = button.getAttribute('value'); function trigger() { button.dispatchEvent(new CustomEvent('clipboard-copy', { bubbles: true })); } if (text) { copyText(text).then(trigger); } else if (id) { const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument; if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return; const node = root.getElementById(id); if (node) copyTarget(node).then(trigger); } } function copyTarget(content) { if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) { return copyText(content.value); } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) { return copyText(content.href); } else { return copyNode(content); } } function clicked(event) { const button = event.currentTarget; if (button instanceof HTMLElement) { copy(button); } } function keydown(event) { if (event.key === ' ' || event.key === 'Enter') { const button = event.currentTarget; if (button instanceof HTMLElement) { event.preventDefault(); copy(button); } } } function focused(event) { event.currentTarget.addEventListener('keydown', keydown); } function blurred(event) { event.currentTarget.removeEventListener('keydown', keydown); } class ClipboardCopyElement extends HTMLElement { constructor() { super(); this.addEventListener('click', clicked); this.addEventListener('focus', focused); this.addEventListener('blur', blurred); } connectedCallback() { if (!this.hasAttribute('tabindex')) { this.setAttribute('tabindex', '0'); } if (!this.hasAttribute('role')) { this.setAttribute('role', 'button'); } } get value() { return 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var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Simple example: data engineering \u00b6 Here, we solve a problem similar to the tutorial's but with an explainable Naive Bayes classifier and more best-practices. In short, we train a domain classifier on the semantic scholar dataset by taking full advantage of great-ai . Subsequently, we create a production-ready deployment. The blue boxes show the steps of a typical AI-development lifecycle implemented in this notebook. Since the true scope of great-ai is the phase between proof-of-concept code and production-ready service, it is predominantly used in the deployment notebook . Feel free to skip there, or continue reading if you'd like to see the full picture. Extract \u00b6 This can be achieved by downloading a public dataset (such as in this case), or by having a Data Engineer setup and give us access to the organisation's data. In this example, we download the semantic scholar dataset from a public S3 bucket. In [1]: Copied! MAX_CHUNK_COUNT = 4 MAX_CHUNK_COUNT = 4 In [2]: Copied! import urllib.request from random import shuffle manifest = ( urllib . request . urlopen ( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"open-corpus/2022-02-01/manifest.txt\" ) . read () . decode () ) # a list of available chunks separated by '\\n' characters lines = manifest . split () shuffle ( lines ) chunks = lines [: MAX_CHUNK_COUNT ] f \"\"\"Processing { len ( chunks ) } out of the { len ( manifest . split ()) } available chunks\"\"\" import urllib.request from random import shuffle manifest = ( urllib.request.urlopen( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"open-corpus/2022-02-01/manifest.txt\" ) .read() .decode() ) # a list of available chunks separated by '\\n' characters lines = manifest.split() shuffle(lines) chunks = lines[:MAX_CHUNK_COUNT] f\"\"\"Processing {len(chunks)} out of the { len(manifest.split()) } available chunks\"\"\" Out[2]: 'Processing 4 out of the 6002 available chunks' Transform \u00b6 Filter out non-English abstracts using great_ai.utilities.predict_language Project it to only keep the necessary components (text and labels), clean the textual content using great_ai.utilities.clean We will speed up processing using great_ai.utilities.simple_parallel_map . In [3]: Copied! from typing import List , Tuple import json import gzip from great_ai.utilities import ( simple_parallel_map , clean , is_english , predict_language , unchunk , ) def preprocess_chunk ( chunk_key : str ) -> List [ Tuple [ str , List [ str ]]]: response = urllib . request . urlopen ( f \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" f \"open-corpus/2022-02-01/ { chunk_key } \" ) # a gzipped JSON Lines file decompressed = gzip . decompress ( response . read ()) decoded = decompressed . decode () chunk = [ json . loads ( line ) for line in decoded . split ( \" \\n \" ) if line ] # Transform return [ ( clean ( f ' { c [ \"title\" ] } { c [ \"paperAbstract\" ] } ' f ' { c [ \"journalName\" ] } { c [ \"venue\" ] } ' , convert_to_ascii = True , ), # The text is cleaned to remove common artifacts c [ \"fieldsOfStudy\" ], ) # Create pairs of `(text, [...domains])` for c in chunk if ( c [ \"fieldsOfStudy\" ] and is_english ( predict_language ( c [ \"paperAbstract\" ]))) ] preprocessed_data = unchunk ( simple_parallel_map ( preprocess_chunk , chunks , concurrency = 4 ) ) from typing import List, Tuple import json import gzip from great_ai.utilities import ( simple_parallel_map, clean, is_english, predict_language, unchunk, ) def preprocess_chunk(chunk_key: str) -> List[Tuple[str, List[str]]]: response = urllib.request.urlopen( f\"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" f\"open-corpus/2022-02-01/{chunk_key}\" ) # a gzipped JSON Lines file decompressed = gzip.decompress(response.read()) decoded = decompressed.decode() chunk = [json.loads(line) for line in decoded.split(\"\\n\") if line] # Transform return [ ( clean( f'{c[\"title\"]} {c[\"paperAbstract\"]} ' f'{c[\"journalName\"]} {c[\"venue\"]}', convert_to_ascii=True, ), # The text is cleaned to remove common artifacts c[\"fieldsOfStudy\"], ) # Create pairs of `(text, [...domains])` for c in chunk if (c[\"fieldsOfStudy\"] and is_english(predict_language(c[\"paperAbstract\"]))) ] preprocessed_data = unchunk( simple_parallel_map(preprocess_chunk, chunks, concurrency=4) ) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4/4 [04:22<00:00, 65.51s/it] In [4]: Copied! X , y = zip ( * preprocessed_data ) # X is the input, y is the expected output X, y = zip(*preprocessed_data) # X is the input, y is the expected output Load \u00b6 Upload the dataset (or a part of it) to a central repository using great_ai.add_ground_truth . This step automatically tags each data-point with a split label according to the ratios we set. Additional tags can be also given. Production-ready backend \u00b6 The MongoDB driver is automatically configured if mongo.ini exists with the following scheme: mongo_connection_string = mongodb://localhost:27017/ mongo_database = my_great_ai_db You can install MongoDB from here or use it as a service Otherwise, TinyDB is used which is just a local JSON file. In [5]: Copied! from great_ai import add_ground_truth add_ground_truth ( X , y , train_split_ratio = 0.8 , test_split_ratio = 0.2 ) from great_ai import add_ground_truth add_ground_truth(X, y, train_split_ratio=0.8, test_split_ratio=0.2) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Next: Part 2 \u00b6","title":"Simple example: data engineering"},{"location":"examples/simple/data/#simple-example-data-engineering","text":"Here, we solve a problem similar to the tutorial's but with an explainable Naive Bayes classifier and more best-practices. In short, we train a domain classifier on the semantic scholar dataset by taking full advantage of great-ai . Subsequently, we create a production-ready deployment. The blue boxes show the steps of a typical AI-development lifecycle implemented in this notebook. Since the true scope of great-ai is the phase between proof-of-concept code and production-ready service, it is predominantly used in the deployment notebook . Feel free to skip there, or continue reading if you'd like to see the full picture.","title":"Simple example: data engineering"},{"location":"examples/simple/data/#extract","text":"This can be achieved by downloading a public dataset (such as in this case), or by having a Data Engineer setup and give us access to the organisation's data. In this example, we download the semantic scholar dataset from a public S3 bucket. In [1]: Copied! MAX_CHUNK_COUNT = 4 MAX_CHUNK_COUNT = 4 In [2]: Copied! import urllib.request from random import shuffle manifest = ( urllib . request . urlopen ( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"open-corpus/2022-02-01/manifest.txt\" ) . read () . decode () ) # a list of available chunks separated by '\\n' characters lines = manifest . split () shuffle ( lines ) chunks = lines [: MAX_CHUNK_COUNT ] f \"\"\"Processing { len ( chunks ) } out of the { len ( manifest . split ()) } available chunks\"\"\" import urllib.request from random import shuffle manifest = ( urllib.request.urlopen( \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" \"open-corpus/2022-02-01/manifest.txt\" ) .read() .decode() ) # a list of available chunks separated by '\\n' characters lines = manifest.split() shuffle(lines) chunks = lines[:MAX_CHUNK_COUNT] f\"\"\"Processing {len(chunks)} out of the { len(manifest.split()) } available chunks\"\"\" Out[2]: 'Processing 4 out of the 6002 available chunks'","title":"Extract"},{"location":"examples/simple/data/#transform","text":"Filter out non-English abstracts using great_ai.utilities.predict_language Project it to only keep the necessary components (text and labels), clean the textual content using great_ai.utilities.clean We will speed up processing using great_ai.utilities.simple_parallel_map . In [3]: Copied! from typing import List , Tuple import json import gzip from great_ai.utilities import ( simple_parallel_map , clean , is_english , predict_language , unchunk , ) def preprocess_chunk ( chunk_key : str ) -> List [ Tuple [ str , List [ str ]]]: response = urllib . request . urlopen ( f \"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" f \"open-corpus/2022-02-01/ { chunk_key } \" ) # a gzipped JSON Lines file decompressed = gzip . decompress ( response . read ()) decoded = decompressed . decode () chunk = [ json . loads ( line ) for line in decoded . split ( \" \\n \" ) if line ] # Transform return [ ( clean ( f ' { c [ \"title\" ] } { c [ \"paperAbstract\" ] } ' f ' { c [ \"journalName\" ] } { c [ \"venue\" ] } ' , convert_to_ascii = True , ), # The text is cleaned to remove common artifacts c [ \"fieldsOfStudy\" ], ) # Create pairs of `(text, [...domains])` for c in chunk if ( c [ \"fieldsOfStudy\" ] and is_english ( predict_language ( c [ \"paperAbstract\" ]))) ] preprocessed_data = unchunk ( simple_parallel_map ( preprocess_chunk , chunks , concurrency = 4 ) ) from typing import List, Tuple import json import gzip from great_ai.utilities import ( simple_parallel_map, clean, is_english, predict_language, unchunk, ) def preprocess_chunk(chunk_key: str) -> List[Tuple[str, List[str]]]: response = urllib.request.urlopen( f\"https://s3-us-west-2.amazonaws.com/ai2-s2-research-public/\" f\"open-corpus/2022-02-01/{chunk_key}\" ) # a gzipped JSON Lines file decompressed = gzip.decompress(response.read()) decoded = decompressed.decode() chunk = [json.loads(line) for line in decoded.split(\"\\n\") if line] # Transform return [ ( clean( f'{c[\"title\"]} {c[\"paperAbstract\"]} ' f'{c[\"journalName\"]} {c[\"venue\"]}', convert_to_ascii=True, ), # The text is cleaned to remove common artifacts c[\"fieldsOfStudy\"], ) # Create pairs of `(text, [...domains])` for c in chunk if (c[\"fieldsOfStudy\"] and is_english(predict_language(c[\"paperAbstract\"]))) ] preprocessed_data = unchunk( simple_parallel_map(preprocess_chunk, chunks, concurrency=4) ) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4/4 [04:22<00:00, 65.51s/it] In [4]: Copied! X , y = zip ( * preprocessed_data ) # X is the input, y is the expected output X, y = zip(*preprocessed_data) # X is the input, y is the expected output","title":"Transform"},{"location":"examples/simple/data/#load","text":"Upload the dataset (or a part of it) to a central repository using great_ai.add_ground_truth . This step automatically tags each data-point with a split label according to the ratios we set. Additional tags can be also given.","title":"Load"},{"location":"examples/simple/data/#production-ready-backend","text":"The MongoDB driver is automatically configured if mongo.ini exists with the following scheme: mongo_connection_string = mongodb://localhost:27017/ mongo_database = my_great_ai_db You can install MongoDB from here or use it as a service Otherwise, TinyDB is used which is just a local JSON file. In [5]: Copied! from great_ai import add_ground_truth add_ground_truth ( X , y , train_split_ratio = 0.8 , test_split_ratio = 0.2 ) from great_ai import add_ground_truth add_ground_truth(X, y, train_split_ratio=0.8, test_split_ratio=0.2) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices","title":"Production-ready backend"},{"location":"examples/simple/data/#next-part-2","text":"","title":"Next: Part 2"},{"location":"examples/simple/deploy/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Hardening and deployment \u00b6 Part 3: Create production inference function The blue boxes show the steps implemented in this notebook. In Part 2 , we trained our AI model. Now, it's time to create G eneral R obust E nd-to-end A utomated T rustworthy deployment. Create the inference function \u00b6 Next to the prediction, we also return the top-n most influential words based on their weights. In [1]: Copied! import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput , ClassificationOutput , GreatAI , use_model , parameter , ) @GreatAI . create @use_model ( \"small-domain-prediction\" , version = \"latest\" ) @parameter ( \"target_confidence\" , validate = lambda c : 0 <= c <= 100 ) def predict_domain ( text : str , model : Pipeline , target_confidence : int = 50 ) -> MultiLabelClassificationOutput : \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re . sub ( r \"[^a-zA-Z\\s]\" , \"\" , clean ( text , convert_to_ascii = True )) features = model . named_steps [ \"vectorizer\" ] . transform ([ preprocessed ]) prediction = model . named_steps [ \"classifier\" ] . predict_proba ( features )[ 0 ] best_classes = sorted ( enumerate ( prediction ), key = lambda v : v [ 1 ], reverse = True ) results = MultiLabelClassificationOutput () for class_index , probability in best_classes : results . labels . append ( get_label ( model = model , features = features , class_index = class_index , probability = probability , ) ) if sum ( r . confidence for r in results . labels ) >= target_confidence : break return results def get_label ( model : Pipeline , features : np . ndarray , class_index : int , probability : float ) -> ClassificationOutput : return ClassificationOutput ( label = model . named_steps [ \"classifier\" ] . classes_ [ class_index ], confidence = round ( probability * 100 ), explanation = [ word for _ , word in sorted ( ( ( weight , word ) for weight , word , count in zip ( model . named_steps [ \"classifier\" ] . feature_log_prob_ [ class_index ], model . named_steps [ \"vectorizer\" ] . get_feature_names_out (), features . A [ 0 ], ) if count > 0 ), reverse = True , ) ][: 5 ], ) import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput, ClassificationOutput, GreatAI, use_model, parameter, ) @GreatAI.create @use_model(\"small-domain-prediction\", version=\"latest\") @parameter(\"target_confidence\", validate=lambda c: 0 < = c < = 100) def predict_domain( text: str, model: Pipeline, target_confidence: int = 50 ) -> MultiLabelClassificationOutput: \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re.sub(r\"[^a-zA-Z\\s]\", \"\", clean(text, convert_to_ascii=True)) features = model.named_steps[\"vectorizer\"].transform([preprocessed]) prediction = model.named_steps[\"classifier\"].predict_proba(features)[0] best_classes = sorted(enumerate(prediction), key=lambda v: v[1], reverse=True) results = MultiLabelClassificationOutput() for class_index, probability in best_classes: results.labels.append( get_label( model=model, features=features, class_index=class_index, probability=probability, ) ) if sum(r.confidence for r in results.labels) >= target_confidence: break return results def get_label( model: Pipeline, features: np.ndarray, class_index: int, probability: float ) -> ClassificationOutput: return ClassificationOutput( label=model.named_steps[\"classifier\"].classes_[class_index], confidence=round(probability * 100), explanation=[ word for _, word in sorted( ( (weight, word) for weight, word, count in zip( model.named_steps[\"classifier\"].feature_log_prob_[class_index], model.named_steps[\"vectorizer\"].get_feature_names_out(), features.A[0], ) if count > 0 ), reverse=True, ) ][:5], ) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of small-domain-prediction Latest version of small-domain-prediction is 2 (from versions: 0, 1, 2) File small-domain-prediction-2 found in cache Check accuracy on the test split \u00b6 Anything under if __name__ == \"__main__\": will not be run when the script is executed by the great-ai CLI app. This, combined with query_ground_truth and the /traces/{trace_id}/feedback endpoint are ideal for creating a continuous-integration job for checking the quality of the model before deployment. In [2]: Copied! if __name__ == \"__main__\" : from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth ( \"test\" ) X = [ d . input for d in data ] y_actual = [ d . feedback for d in data ] y_predicted = [ d . output . labels [ 0 ] . label for d in predict_domain . process_batch ( X , do_not_persist_traces = True ) ] y_actual_aligned = [ p if p in a else a [ 0 ] for p , a in zip ( y_predicted , y_actual )] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 19 , 18 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 16 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_actual_aligned , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_actual_aligned , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) plt . tight_layout () plt . savefig ( \"ss-confusion.png\" , dpi = 600 ) if __name__ == \"__main__\": from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth(\"test\") X = [d.input for d in data] y_actual = [d.feedback for d in data] y_predicted = [ d.output.labels[0].label for d in predict_domain.process_batch(X, do_not_persist_traces=True) ] y_actual_aligned = [p if p in a else a[0] for p, a in zip(y_predicted, y_actual)] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.figsize\"] = (19, 18) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 16 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_actual_aligned, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_actual_aligned, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) plt.tight_layout() plt.savefig(\"ss-confusion.png\", dpi=600) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 12272/12272 [01:10<00:00, 174.77it/s] precision recall f1-score support Art 0.54 0.38 0.45 126 Biology 0.77 0.84 0.80 1215 Business 0.47 0.73 0.57 311 Chemistry 0.82 0.67 0.74 1205 Computer Science 0.77 0.76 0.76 1277 Economics 0.69 0.55 0.61 270 Engineering 0.55 0.52 0.53 754 Environmental Science 0.56 0.55 0.55 227 Geography 0.54 0.39 0.45 276 Geology 0.74 0.67 0.70 265 History 0.35 0.18 0.24 140 Materials Science 0.72 0.81 0.76 1011 Mathematics 0.77 0.70 0.74 498 Medicine 0.96 0.77 0.86 2835 Philosophy 0.57 0.06 0.10 71 Physics 0.66 0.75 0.70 611 Political Science 0.44 0.61 0.51 291 Psychology 0.52 0.84 0.64 574 Sociology 0.33 0.59 0.42 315 accuracy 0.71 12272 macro avg 0.62 0.60 0.59 12272 weighted avg 0.74 0.71 0.71 12272","title":"Hardening and deployment"},{"location":"examples/simple/deploy/#hardening-and-deployment","text":"Part 3: Create production inference function The blue boxes show the steps implemented in this notebook. In Part 2 , we trained our AI model. Now, it's time to create G eneral R obust E nd-to-end A utomated T rustworthy deployment.","title":"Hardening and deployment"},{"location":"examples/simple/deploy/#create-the-inference-function","text":"Next to the prediction, we also return the top-n most influential words based on their weights. In [1]: Copied! import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput , ClassificationOutput , GreatAI , use_model , parameter , ) @GreatAI . create @use_model ( \"small-domain-prediction\" , version = \"latest\" ) @parameter ( \"target_confidence\" , validate = lambda c : 0 <= c <= 100 ) def predict_domain ( text : str , model : Pipeline , target_confidence : int = 50 ) -> MultiLabelClassificationOutput : \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re . sub ( r \"[^a-zA-Z\\s]\" , \"\" , clean ( text , convert_to_ascii = True )) features = model . named_steps [ \"vectorizer\" ] . transform ([ preprocessed ]) prediction = model . named_steps [ \"classifier\" ] . predict_proba ( features )[ 0 ] best_classes = sorted ( enumerate ( prediction ), key = lambda v : v [ 1 ], reverse = True ) results = MultiLabelClassificationOutput () for class_index , probability in best_classes : results . labels . append ( get_label ( model = model , features = features , class_index = class_index , probability = probability , ) ) if sum ( r . confidence for r in results . labels ) >= target_confidence : break return results def get_label ( model : Pipeline , features : np . ndarray , class_index : int , probability : float ) -> ClassificationOutput : return ClassificationOutput ( label = model . named_steps [ \"classifier\" ] . classes_ [ class_index ], confidence = round ( probability * 100 ), explanation = [ word for _ , word in sorted ( ( ( weight , word ) for weight , word , count in zip ( model . named_steps [ \"classifier\" ] . feature_log_prob_ [ class_index ], model . named_steps [ \"vectorizer\" ] . get_feature_names_out (), features . A [ 0 ], ) if count > 0 ), reverse = True , ) ][: 5 ], ) import re import numpy as np from sklearn.pipeline import Pipeline from great_ai.utilities import clean from great_ai import ( MultiLabelClassificationOutput, ClassificationOutput, GreatAI, use_model, parameter, ) @GreatAI.create @use_model(\"small-domain-prediction\", version=\"latest\") @parameter(\"target_confidence\", validate=lambda c: 0 < = c < = 100) def predict_domain( text: str, model: Pipeline, target_confidence: int = 50 ) -> MultiLabelClassificationOutput: \"\"\" Predict the scientific domain of the input text. Return labels until their sum likelihood is larger than `target_confidence`. \"\"\" preprocessed = re.sub(r\"[^a-zA-Z\\s]\", \"\", clean(text, convert_to_ascii=True)) features = model.named_steps[\"vectorizer\"].transform([preprocessed]) prediction = model.named_steps[\"classifier\"].predict_proba(features)[0] best_classes = sorted(enumerate(prediction), key=lambda v: v[1], reverse=True) results = MultiLabelClassificationOutput() for class_index, probability in best_classes: results.labels.append( get_label( model=model, features=features, class_index=class_index, probability=probability, ) ) if sum(r.confidence for r in results.labels) >= target_confidence: break return results def get_label( model: Pipeline, features: np.ndarray, class_index: int, probability: float ) -> ClassificationOutput: return ClassificationOutput( label=model.named_steps[\"classifier\"].classes_[class_index], confidence=round(probability * 100), explanation=[ word for _, word in sorted( ( (weight, word) for weight, word, count in zip( model.named_steps[\"classifier\"].feature_log_prob_[class_index], model.named_steps[\"vectorizer\"].get_feature_names_out(), features.A[0], ) if count > 0 ), reverse=True, ) ][:5], ) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of small-domain-prediction Latest version of small-domain-prediction is 2 (from versions: 0, 1, 2) File small-domain-prediction-2 found in cache","title":"Create the inference function"},{"location":"examples/simple/deploy/#check-accuracy-on-the-test-split","text":"Anything under if __name__ == \"__main__\": will not be run when the script is executed by the great-ai CLI app. This, combined with query_ground_truth and the /traces/{trace_id}/feedback endpoint are ideal for creating a continuous-integration job for checking the quality of the model before deployment. In [2]: Copied! if __name__ == \"__main__\" : from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth ( \"test\" ) X = [ d . input for d in data ] y_actual = [ d . feedback for d in data ] y_predicted = [ d . output . labels [ 0 ] . label for d in predict_domain . process_batch ( X , do_not_persist_traces = True ) ] y_actual_aligned = [ p if p in a else a [ 0 ] for p , a in zip ( y_predicted , y_actual )] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 19 , 18 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 16 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_actual_aligned , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_actual_aligned , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) plt . tight_layout () plt . savefig ( \"ss-confusion.png\" , dpi = 600 ) if __name__ == \"__main__\": from great_ai import query_ground_truth from sklearn import metrics data = query_ground_truth(\"test\") X = [d.input for d in data] y_actual = [d.feedback for d in data] y_predicted = [ d.output.labels[0].label for d in predict_domain.process_batch(X, do_not_persist_traces=True) ] y_actual_aligned = [p if p in a else a[0] for p, a in zip(y_predicted, y_actual)] import matplotlib.pyplot as plt # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.figsize\"] = (19, 18) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 16 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_actual_aligned, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_actual_aligned, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) plt.tight_layout() plt.savefig(\"ss-confusion.png\", dpi=600) 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 12272/12272 [01:10<00:00, 174.77it/s] precision recall f1-score support Art 0.54 0.38 0.45 126 Biology 0.77 0.84 0.80 1215 Business 0.47 0.73 0.57 311 Chemistry 0.82 0.67 0.74 1205 Computer Science 0.77 0.76 0.76 1277 Economics 0.69 0.55 0.61 270 Engineering 0.55 0.52 0.53 754 Environmental Science 0.56 0.55 0.55 227 Geography 0.54 0.39 0.45 276 Geology 0.74 0.67 0.70 265 History 0.35 0.18 0.24 140 Materials Science 0.72 0.81 0.76 1011 Mathematics 0.77 0.70 0.74 498 Medicine 0.96 0.77 0.86 2835 Philosophy 0.57 0.06 0.10 71 Physics 0.66 0.75 0.70 611 Political Science 0.44 0.61 0.51 291 Psychology 0.52 0.84 0.64 574 Sociology 0.33 0.59 0.42 315 accuracy 0.71 12272 macro avg 0.62 0.60 0.59 12272 weighted avg 0.74 0.71 0.71 12272","title":"Check accuracy on the test split"},{"location":"examples/simple/stacked-bars/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); In [1]: Copied! from great_ai import query_ground_truth data = query_ground_truth ( \"train\" ) from great_ai import query_ground_truth data = query_ground_truth(\"train\") Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices In [9]: Copied! from collections import Counter import matplotlib.pyplot as plt from great_ai.utilities import unique import numpy as np import matplotlib.ticker as mtick first = Counter ( d . feedback [ 0 ] for d in data if len ( d . feedback ) > 0 ) second = Counter ( d . feedback [ 1 ] for d in data if len ( d . feedback ) > 1 ) third = Counter ( d . feedback [ 2 ] for d in data if len ( d . feedback ) > 2 ) domains = sorted ( unique ( domain for d in data for domain in d . feedback )) first = [ first [ d ] / len ( data ) for d in domains ] second = [ second [ d ] / len ( data ) for d in domains ] third = [ third [ d ] / len ( data ) for d in domains ] # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 20 plt . rcParams [ \"figure.figsize\" ] = ( 14 , 10 ) fig , ax = plt . subplots () plt . xticks ( rotation = 90 ) ax . bar ( domains , first , label = \"Primary domain\" ) ax . bar ( domains , second , label = \"Secondary domain\" , bottom = first ) ax . bar ( domains , third , label = \"Tertiary domain\" , bottom = np . add ( first , second )) ax . yaxis . set_major_formatter ( mtick . PercentFormatter ( 1 )) ax . legend () ax . set_ylabel ( \"Ratio of all publications\" ) fig . tight_layout () fig . savefig ( \"ss-distribution.png\" , dpi = 500 ) None from collections import Counter import matplotlib.pyplot as plt from great_ai.utilities import unique import numpy as np import matplotlib.ticker as mtick first = Counter(d.feedback[0] for d in data if len(d.feedback) > 0) second = Counter(d.feedback[1] for d in data if len(d.feedback) > 1) third = Counter(d.feedback[2] for d in data if len(d.feedback) > 2) domains = sorted(unique(domain for d in data for domain in d.feedback)) first = [first[d] / len(data) for d in domains] second = [second[d] / len(data) for d in domains] third = [third[d] / len(data) for d in domains] # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 20 plt.rcParams[\"figure.figsize\"] = (14, 10) fig, ax = plt.subplots() plt.xticks(rotation=90) ax.bar(domains, first, label=\"Primary domain\") ax.bar(domains, second, label=\"Secondary domain\", bottom=first) ax.bar(domains, third, label=\"Tertiary domain\", bottom=np.add(first, second)) ax.yaxis.set_major_formatter(mtick.PercentFormatter(1)) ax.legend() ax.set_ylabel(\"Ratio of all publications\") fig.tight_layout() fig.savefig(\"ss-distribution.png\", dpi=500) None","title":"Stacked bars"},{"location":"examples/simple/train/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); const range = document.createRange(); range.selectNodeContents(node); selection.addRange(range); document.execCommand('copy'); selection.removeAllRanges(); return Promise.resolve(); } function copyText(text) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(text); } const body = document.body; if (!body) { return Promise.reject(new Error()); } const node = createNode(text); body.appendChild(node); copyNode(node); body.removeChild(node); return Promise.resolve(); } function copy(button) { const id = button.getAttribute('for'); const text = button.getAttribute('value'); function trigger() { button.dispatchEvent(new CustomEvent('clipboard-copy', { bubbles: true })); } if (text) { copyText(text).then(trigger); } else if (id) { const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument; if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return; const node = root.getElementById(id); if (node) copyTarget(node).then(trigger); } } function copyTarget(content) { if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) { return copyText(content.value); } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) { return copyText(content.href); } else { return copyNode(content); } } function clicked(event) { const button = event.currentTarget; if (button instanceof HTMLElement) { copy(button); } } function keydown(event) { if (event.key === ' ' || event.key === 'Enter') { const button = event.currentTarget; if (button instanceof HTMLElement) { event.preventDefault(); copy(button); } } } function focused(event) { event.currentTarget.addEventListener('keydown', keydown); } function blurred(event) { event.currentTarget.removeEventListener('keydown', keydown); } class ClipboardCopyElement extends HTMLElement { constructor() { super(); this.addEventListener('click', clicked); this.addEventListener('focus', focused); this.addEventListener('blur', blurred); } connectedCallback() { if (!this.hasAttribute('tabindex')) { this.setAttribute('tabindex', '0'); } if (!this.hasAttribute('role')) { this.setAttribute('role', 'button'); } } get value() { return this.getAttribute('value') || ''; } set value(text) { this.setAttribute('value', text); } } if (!window.customElements.get('clipboard-copy')) { window.ClipboardCopyElement = ClipboardCopyElement; window.customElements.define('clipboard-copy', ClipboardCopyElement); } return ClipboardCopyElement; })); document.addEventListener('clipboard-copy', function(event) { const notice = event.target.querySelector('.notice') notice.hidden = false setTimeout(function() { notice.hidden = true }, 1000) }) pre { line-height: 125%; } td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Optimise and train a model \u00b6 The blue boxes show the steps implemented in this notebook. In the first part, we have cleaned and transformed our training data. We can now access this data using great_ai.LargeFile . Locally, it will gives us the cached version, otherwise, the latest version is downloaded from S3 or GridFS. In this part, we hyperparameter-optimise and train a simple Naive Bayes classifier which we then export for deployment using great_ai.save_model . Load data that has been extracted in part 1 \u00b6 In [1]: Copied! from great_ai import query_ground_truth data = query_ground_truth ( \"train\" ) X = [ d . input for d in data for domain in d . feedback ] y = [ domain for d in data for domain in d . feedback ] from great_ai import query_ground_truth data = query_ground_truth(\"train\") X = [d.input for d in data for domain in d.feedback] y = [domain for d in data for domain in d.feedback] Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices In [2]: Copied! from collections import Counter import matplotlib.pyplot as plt domains , counts = zip ( * Counter ( y ) . most_common ()) # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 20 , 5 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 plt . xticks ( rotation = 90 ) plt . bar ( domains , counts ) None from collections import Counter import matplotlib.pyplot as plt domains, counts = zip(*Counter(y).most_common()) # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.figsize\"] = (20, 5) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 plt.xticks(rotation=90) plt.bar(domains, counts) None Optimise and train Multinomial Naive Bayes classifier \u00b6 In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X , y ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X, y) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 7 14.549476 0.361685 8.476837 0.222398 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.518061 0.514842 0.511599 0.514834 0.002638 1 10 11.235289 0.130426 4.092868 0.082518 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.513897 0.515661 0.507867 0.512475 0.003337 2 19 7.383645 0.138110 4.130709 0.250048 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.496825 0.501045 0.496854 0.498241 0.001983 3 11 10.435154 0.305144 3.882101 0.128886 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.493247 0.497814 0.502245 0.497769 0.003674 4 8 13.643193 0.310696 4.173707 0.142980 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.489609 0.495207 0.498154 0.494323 0.003544 5 22 7.048340 0.050070 3.172948 0.152418 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.487456 0.493865 0.491157 0.490826 0.002627 6 23 7.564685 0.146092 2.374111 0.285026 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.485160 0.494039 0.490127 0.489776 0.003633 7 20 7.172353 0.212599 3.747219 0.130217 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.481303 0.490002 0.488269 0.486524 0.003759 8 6 14.276345 0.456576 8.318859 0.268701 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.482429 0.487735 0.484888 0.485017 0.002168 9 2 14.902358 0.737693 5.975091 0.171150 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.469598 0.474490 0.473637 0.472575 0.002134 10 9 12.677349 0.145143 4.374204 0.175674 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.468872 0.476451 0.470921 0.472082 0.003201 11 5 13.423686 0.482872 8.008324 0.442975 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.465726 0.474548 0.471879 0.470718 0.003694 12 1 13.819117 0.838347 6.161175 0.336590 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.463395 0.473982 0.471262 0.469546 0.004489 13 4 13.281476 0.588822 8.335852 0.254627 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.458734 0.468053 0.464418 0.463735 0.003835 14 14 7.282247 0.444940 3.567094 0.044519 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.438189 0.450160 0.446180 0.444843 0.004978 15 17 7.098797 0.196241 3.838628 0.091128 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.436488 0.444503 0.445900 0.442297 0.004147 16 18 7.492791 0.288889 3.843224 0.073438 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.428196 0.431945 0.430160 0.430100 0.001531 17 21 7.229826 0.099823 3.656332 0.073780 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.403130 0.410170 0.409801 0.407700 0.003235 18 13 7.158370 0.169818 3.765632 0.082924 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.399237 0.412872 0.407982 0.406697 0.005640 19 16 7.064643 0.119529 3.810983 0.125897 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.388060 0.399247 0.396325 0.394544 0.004737 20 0 13.749660 0.465174 6.407841 0.549166 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.384852 0.386487 0.386796 0.386045 0.000853 21 3 15.954147 0.318013 6.337361 0.261697 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.369785 0.375645 0.375858 0.373763 0.002814 22 12 7.120198 0.050452 3.833905 0.069540 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.277741 0.280564 0.285337 0.281214 0.003135 23 15 7.497707 0.183054 3.870714 0.062888 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.255578 0.263381 0.266184 0.261714 0.004487 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X , y ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X, y) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=0.5, fit_prior=False))]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=0.5, fit_prior=False))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=0.5, fit_prior=False) Export the model using GreatAI \u00b6 In [6]: Copied! from great_ai import save_model save_model ( classifier , key = \"small-domain-prediction\" , keep_last_n = 5 ) from great_ai import save_model save_model(classifier, key=\"small-domain-prediction\", keep_last_n=5) Fetching cached versions of small-domain-prediction Copying file for small-domain-prediction-2 Compressing small-domain-prediction-2 Model small-domain-prediction uploaded with version 2 Out[6]: 'small-domain-prediction:2' Next: Part 3 \u00b6","title":"Optimise and train a model"},{"location":"examples/simple/train/#optimise-and-train-a-model","text":"The blue boxes show the steps implemented in this notebook. In the first part, we have cleaned and transformed our training data. We can now access this data using great_ai.LargeFile . Locally, it will gives us the cached version, otherwise, the latest version is downloaded from S3 or GridFS. In this part, we hyperparameter-optimise and train a simple Naive Bayes classifier which we then export for deployment using great_ai.save_model .","title":"Optimise and train a model"},{"location":"examples/simple/train/#load-data-that-has-been-extracted-in-part-1","text":"In [1]: Copied! from great_ai import query_ground_truth data = query_ground_truth ( \"train\" ) X = [ d . input for d in data for domain in d . feedback ] y = [ domain for d in data for domain in d . feedback ] from great_ai import query_ground_truth data = query_ground_truth(\"train\") X = [d.input for d in data for domain in d.feedback] y = [domain for d in data for domain in d.feedback] Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.6): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices In [2]: Copied! from collections import Counter import matplotlib.pyplot as plt domains , counts = zip ( * Counter ( y ) . most_common ()) # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 20 , 5 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 plt . xticks ( rotation = 90 ) plt . bar ( domains , counts ) None from collections import Counter import matplotlib.pyplot as plt domains, counts = zip(*Counter(y).most_common()) # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.figsize\"] = (20, 5) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 plt.xticks(rotation=90) plt.bar(domains, counts) None","title":"Load data that has been extracted in part 1"},{"location":"examples/simple/train/#optimise-and-train-multinomial-naive-bayes-classifier","text":"In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X , y ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X, y) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 7 14.549476 0.361685 8.476837 0.222398 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.518061 0.514842 0.511599 0.514834 0.002638 1 10 11.235289 0.130426 4.092868 0.082518 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.513897 0.515661 0.507867 0.512475 0.003337 2 19 7.383645 0.138110 4.130709 0.250048 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.496825 0.501045 0.496854 0.498241 0.001983 3 11 10.435154 0.305144 3.882101 0.128886 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.493247 0.497814 0.502245 0.497769 0.003674 4 8 13.643193 0.310696 4.173707 0.142980 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.489609 0.495207 0.498154 0.494323 0.003544 5 22 7.048340 0.050070 3.172948 0.152418 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.487456 0.493865 0.491157 0.490826 0.002627 6 23 7.564685 0.146092 2.374111 0.285026 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.485160 0.494039 0.490127 0.489776 0.003633 7 20 7.172353 0.212599 3.747219 0.130217 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.481303 0.490002 0.488269 0.486524 0.003759 8 6 14.276345 0.456576 8.318859 0.268701 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.482429 0.487735 0.484888 0.485017 0.002168 9 2 14.902358 0.737693 5.975091 0.171150 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.469598 0.474490 0.473637 0.472575 0.002134 10 9 12.677349 0.145143 4.374204 0.175674 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.468872 0.476451 0.470921 0.472082 0.003201 11 5 13.423686 0.482872 8.008324 0.442975 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.465726 0.474548 0.471879 0.470718 0.003694 12 1 13.819117 0.838347 6.161175 0.336590 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.463395 0.473982 0.471262 0.469546 0.004489 13 4 13.281476 0.588822 8.335852 0.254627 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.458734 0.468053 0.464418 0.463735 0.003835 14 14 7.282247 0.444940 3.567094 0.044519 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.438189 0.450160 0.446180 0.444843 0.004978 15 17 7.098797 0.196241 3.838628 0.091128 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.436488 0.444503 0.445900 0.442297 0.004147 16 18 7.492791 0.288889 3.843224 0.073438 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.428196 0.431945 0.430160 0.430100 0.001531 17 21 7.229826 0.099823 3.656332 0.073780 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.403130 0.410170 0.409801 0.407700 0.003235 18 13 7.158370 0.169818 3.765632 0.082924 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.399237 0.412872 0.407982 0.406697 0.005640 19 16 7.064643 0.119529 3.810983 0.125897 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.388060 0.399247 0.396325 0.394544 0.004737 20 0 13.749660 0.465174 6.407841 0.549166 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.384852 0.386487 0.386796 0.386045 0.000853 21 3 15.954147 0.318013 6.337361 0.261697 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.369785 0.375645 0.375858 0.373763 0.002814 22 12 7.120198 0.050452 3.833905 0.069540 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.277741 0.280564 0.285337 0.281214 0.003135 23 15 7.497707 0.183054 3.870714 0.062888 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.255578 0.263381 0.266184 0.261714 0.004487 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X , y ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X, y) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. 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Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=0.5, fit_prior=False))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=20, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=0.5, fit_prior=False)","title":"Optimise and train Multinomial Naive Bayes classifier"},{"location":"examples/simple/train/#export-the-model-using-greatai","text":"In [6]: Copied! from great_ai import save_model save_model ( classifier , key = \"small-domain-prediction\" , keep_last_n = 5 ) from great_ai import save_model save_model(classifier, key=\"small-domain-prediction\", keep_last_n=5) Fetching cached versions of small-domain-prediction Copying file for small-domain-prediction-2 Compressing small-domain-prediction-2 Model small-domain-prediction uploaded with version 2 Out[6]: 'small-domain-prediction:2'","title":"Export the model using GreatAI"},{"location":"examples/simple/train/#next-part-3","text":"","title":"Next: Part 3"},{"location":"examples/simple-mag/train/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Train a field of study (domain) classification of sentences on the MAG dataset \u00b6 SciBERT achieves an F1-score of 0.6571 on this dataset. This notebook shows that better results can be achieved without even using transformers. In [1]: Copied! import json from typing import Tuple from great_ai.utilities import clean , simple_parallel_map from tqdm.cli import tqdm def preprocess ( line : str ) -> Tuple [ str , str ]: data_point = json . loads ( line ) return ( clean ( data_point [ \"text\" ], convert_to_ascii = True ), data_point [ \"label\" ]) with open ( \"../../tutorial/data/train.txt\" , encoding = \"utf-8\" ) as f : training_data = simple_parallel_map ( preprocess , f . readlines ()) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] with open ( \"../../tutorial/data/test.txt\" , encoding = \"utf-8\" ) as f : test_data = simple_parallel_map ( preprocess , f . readlines ()) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] import json from typing import Tuple from great_ai.utilities import clean, simple_parallel_map from tqdm.cli import tqdm def preprocess(line: str) -> Tuple[str, str]: data_point = json.loads(line) return (clean(data_point[\"text\"], convert_to_ascii=True), data_point[\"label\"]) with open(\"../../tutorial/data/train.txt\", encoding=\"utf-8\") as f: training_data = simple_parallel_map(preprocess, f.readlines()) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] with open(\"../../tutorial/data/test.txt\", encoding=\"utf-8\") as f: test_data = simple_parallel_map(preprocess, f.readlines()) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84000/84000 [00:16<00:00, 4964.08it/s] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22399/22399 [00:05<00:00, 3847.04it/s] In [2]: Copied! from collections import Counter import matplotlib.pyplot as plt domains , counts = zip ( * Counter ( y_train ) . most_common ()) # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 18 plt . rcParams [ \"figure.figsize\" ] = ( 8 , 5 ) fig , ax = plt . subplots () plt . xticks ( rotation = 90 ) ax . bar ( domains , counts ) ax . set_ylabel ( \"Count\" ) fig . tight_layout () fig . savefig ( \"mag-distribution.png\" , dpi = 500 ) None from collections import Counter import matplotlib.pyplot as plt domains, counts = zip(*Counter(y_train).most_common()) # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 18 plt.rcParams[\"figure.figsize\"] = (8, 5) fig, ax = plt.subplots() plt.xticks(rotation=90) ax.bar(domains, counts) ax.set_ylabel(\"Count\") fig.tight_layout() fig.savefig(\"mag-distribution.png\", dpi=500) None In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X_train , y_train ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X_train, y_train) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 12 2.352721 0.257936 1.357399 0.094580 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.677283 0.670398 0.665671 0.671118 0.004768 1 15 2.544958 0.051976 1.245658 0.137108 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.676970 0.670244 0.666123 0.671112 0.004470 2 18 2.472888 0.053978 1.447181 0.051170 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675678 0.669321 0.665305 0.670101 0.004271 3 21 2.467637 0.005358 1.216254 0.048319 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675569 0.669482 0.665225 0.670092 0.004245 4 3 2.477612 0.134880 1.428521 0.017417 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675225 0.668912 0.665652 0.669930 0.003974 5 0 2.484810 0.067836 1.249979 0.209959 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675264 0.668781 0.665394 0.669813 0.004095 6 9 2.452453 0.049376 1.279694 0.089200 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.674199 0.668005 0.664456 0.668887 0.004026 7 6 2.748628 0.098077 1.255213 0.101334 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.673996 0.668048 0.664572 0.668872 0.003891 8 13 2.371907 0.204820 1.375143 0.193984 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656459 0.647433 0.644864 0.649585 0.004972 9 16 2.557486 0.184332 1.272137 0.133980 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656196 0.646960 0.645436 0.649530 0.004754 10 4 2.362093 0.109796 1.344960 0.083562 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654424 0.644690 0.644991 0.648035 0.004519 11 1 2.668012 0.131751 1.383441 0.049157 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654622 0.644901 0.644425 0.647983 0.004699 12 19 2.517576 0.154192 1.374269 0.026773 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.655125 0.645586 0.643166 0.647959 0.005163 13 22 2.386240 0.069718 0.967638 0.076766 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.654254 0.645409 0.643514 0.647726 0.004680 14 7 2.416211 0.138001 1.316785 0.036402 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652727 0.643691 0.642749 0.646389 0.004498 15 10 2.397768 0.244951 1.330007 0.008983 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652290 0.642975 0.643421 0.646229 0.004290 16 14 2.590127 0.054396 1.155976 0.187602 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.587097 0.577411 0.580170 0.581560 0.004075 17 17 2.497556 0.085952 1.169808 0.061336 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.585713 0.577197 0.580763 0.581224 0.003492 18 2 2.467611 0.168399 1.313936 0.109695 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.586346 0.576987 0.579852 0.581062 0.003915 19 5 2.339322 0.092595 1.209515 0.132825 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.585634 0.576217 0.580320 0.580723 0.003855 20 20 2.644488 0.119967 1.238554 0.048871 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.581096 0.572833 0.576280 0.576736 0.003389 21 8 2.456030 0.072136 1.240463 0.092383 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.580841 0.572391 0.575731 0.576321 0.003475 22 23 2.335796 0.082338 0.786187 0.073499 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.579769 0.572373 0.576699 0.576280 0.003034 23 11 2.530331 0.107199 1.247842 0.078229 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.579743 0.571607 0.576352 0.575901 0.003337 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X_train , y_train ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X_train, y_train) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=1) In [12]: Copied! from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier . predict ( X_test ) % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 20 , 18 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 22 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_test , y_predicted , digits = 4 )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_test , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) plt . tight_layout () plt . savefig ( \"mag-confusion.png\" , dpi = 600 ) from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier.predict(X_test) %matplotlib inline plt.rcParams[\"figure.figsize\"] = (20, 18) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 22 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_test, y_predicted, digits=4)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_test, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) plt.tight_layout() plt.savefig(\"mag-confusion.png\", dpi=600) precision recall f1-score support business 0.6412 0.7258 0.6808 3198 economics 0.6635 0.7146 0.6881 3189 geography 0.7461 0.6791 0.7111 3207 medicine 0.8806 0.9021 0.8912 3187 politics 0.5563 0.5895 0.5724 3169 psychology 0.7654 0.6811 0.7208 3252 sociology 0.5165 0.4698 0.4921 3197 accuracy 0.6803 22399 macro avg 0.6814 0.6803 0.6795 22399 weighted avg 0.6817 0.6803 0.6796 22399","title":"Train a field of study (domain) classification of sentences on the MAG dataset"},{"location":"examples/simple-mag/train/#train-a-field-of-study-domain-classification-of-sentences-on-the-mag-dataset","text":"SciBERT achieves an F1-score of 0.6571 on this dataset. This notebook shows that better results can be achieved without even using transformers. In [1]: Copied! import json from typing import Tuple from great_ai.utilities import clean , simple_parallel_map from tqdm.cli import tqdm def preprocess ( line : str ) -> Tuple [ str , str ]: data_point = json . loads ( line ) return ( clean ( data_point [ \"text\" ], convert_to_ascii = True ), data_point [ \"label\" ]) with open ( \"../../tutorial/data/train.txt\" , encoding = \"utf-8\" ) as f : training_data = simple_parallel_map ( preprocess , f . readlines ()) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] with open ( \"../../tutorial/data/test.txt\" , encoding = \"utf-8\" ) as f : test_data = simple_parallel_map ( preprocess , f . readlines ()) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] import json from typing import Tuple from great_ai.utilities import clean, simple_parallel_map from tqdm.cli import tqdm def preprocess(line: str) -> Tuple[str, str]: data_point = json.loads(line) return (clean(data_point[\"text\"], convert_to_ascii=True), data_point[\"label\"]) with open(\"../../tutorial/data/train.txt\", encoding=\"utf-8\") as f: training_data = simple_parallel_map(preprocess, f.readlines()) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] with open(\"../../tutorial/data/test.txt\", encoding=\"utf-8\") as f: test_data = simple_parallel_map(preprocess, f.readlines()) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84000/84000 [00:16<00:00, 4964.08it/s] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22399/22399 [00:05<00:00, 3847.04it/s] In [2]: Copied! from collections import Counter import matplotlib.pyplot as plt domains , counts = zip ( * Counter ( y_train ) . most_common ()) # Configure matplotlib to have nice, high-resolution charts % matplotlib inline plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 18 plt . rcParams [ \"figure.figsize\" ] = ( 8 , 5 ) fig , ax = plt . subplots () plt . xticks ( rotation = 90 ) ax . bar ( domains , counts ) ax . set_ylabel ( \"Count\" ) fig . tight_layout () fig . savefig ( \"mag-distribution.png\" , dpi = 500 ) None from collections import Counter import matplotlib.pyplot as plt domains, counts = zip(*Counter(y_train).most_common()) # Configure matplotlib to have nice, high-resolution charts %matplotlib inline plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 18 plt.rcParams[\"figure.figsize\"] = (8, 5) fig, ax = plt.subplots() plt.xticks(rotation=90) ax.bar(domains, counts) ax.set_ylabel(\"Count\") fig.tight_layout() fig.savefig(\"mag-distribution.png\", dpi=500) None In [3]: Copied! from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline () -> Pipeline : return Pipeline ( steps = [ ( \"vectorizer\" , TfidfVectorizer ( sublinear_tf = True )), ( \"classifier\" , MultinomialNB ()), ] ) from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer def create_pipeline() -> Pipeline: return Pipeline( steps=[ (\"vectorizer\", TfidfVectorizer(sublinear_tf=True)), (\"classifier\", MultinomialNB()), ] ) In [4]: Copied! from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV ( create_pipeline (), { \"vectorizer__min_df\" : [ 5 , 20 , 100 ], \"vectorizer__max_df\" : [ 0.05 , 0.1 ], \"classifier__alpha\" : [ 0.5 , 1 ], \"classifier__fit_prior\" : [ True , False ], }, scoring = \"f1_macro\" , cv = 3 , n_jobs =- 1 , verbose = 1 , ) optimisation_pipeline . fit ( X_train , y_train ) results = pd . DataFrame ( optimisation_pipeline . cv_results_ ) results . sort_values ( \"rank_test_score\" ) from sklearn.model_selection import GridSearchCV import pandas as pd optimisation_pipeline = GridSearchCV( create_pipeline(), { \"vectorizer__min_df\": [5, 20, 100], \"vectorizer__max_df\": [0.05, 0.1], \"classifier__alpha\": [0.5, 1], \"classifier__fit_prior\": [True, False], }, scoring=\"f1_macro\", cv=3, n_jobs=-1, verbose=1, ) optimisation_pipeline.fit(X_train, y_train) results = pd.DataFrame(optimisation_pipeline.cv_results_) results.sort_values(\"rank_test_score\") Fitting 3 folds for each of 24 candidates, totalling 72 fits Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } mean_fit_time std_fit_time mean_score_time std_score_time param_classifier__alpha param_classifier__fit_prior param_vectorizer__max_df param_vectorizer__min_df params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score 12 2.352721 0.257936 1.357399 0.094580 1 True 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.677283 0.670398 0.665671 0.671118 0.004768 1 15 2.544958 0.051976 1.245658 0.137108 1 True 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.676970 0.670244 0.666123 0.671112 0.004470 2 18 2.472888 0.053978 1.447181 0.051170 1 False 0.05 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675678 0.669321 0.665305 0.670101 0.004271 3 21 2.467637 0.005358 1.216254 0.048319 1 False 0.1 5 {'classifier__alpha': 1, 'classifier__fit_prio... 0.675569 0.669482 0.665225 0.670092 0.004245 4 3 2.477612 0.134880 1.428521 0.017417 0.5 True 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675225 0.668912 0.665652 0.669930 0.003974 5 0 2.484810 0.067836 1.249979 0.209959 0.5 True 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.675264 0.668781 0.665394 0.669813 0.004095 6 9 2.452453 0.049376 1.279694 0.089200 0.5 False 0.1 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.674199 0.668005 0.664456 0.668887 0.004026 7 6 2.748628 0.098077 1.255213 0.101334 0.5 False 0.05 5 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.673996 0.668048 0.664572 0.668872 0.003891 8 13 2.371907 0.204820 1.375143 0.193984 1 True 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656459 0.647433 0.644864 0.649585 0.004972 9 16 2.557486 0.184332 1.272137 0.133980 1 True 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.656196 0.646960 0.645436 0.649530 0.004754 10 4 2.362093 0.109796 1.344960 0.083562 0.5 True 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654424 0.644690 0.644991 0.648035 0.004519 11 1 2.668012 0.131751 1.383441 0.049157 0.5 True 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.654622 0.644901 0.644425 0.647983 0.004699 12 19 2.517576 0.154192 1.374269 0.026773 1 False 0.05 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.655125 0.645586 0.643166 0.647959 0.005163 13 22 2.386240 0.069718 0.967638 0.076766 1 False 0.1 20 {'classifier__alpha': 1, 'classifier__fit_prio... 0.654254 0.645409 0.643514 0.647726 0.004680 14 7 2.416211 0.138001 1.316785 0.036402 0.5 False 0.05 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652727 0.643691 0.642749 0.646389 0.004498 15 10 2.397768 0.244951 1.330007 0.008983 0.5 False 0.1 20 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.652290 0.642975 0.643421 0.646229 0.004290 16 14 2.590127 0.054396 1.155976 0.187602 1 True 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.587097 0.577411 0.580170 0.581560 0.004075 17 17 2.497556 0.085952 1.169808 0.061336 1 True 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.585713 0.577197 0.580763 0.581224 0.003492 18 2 2.467611 0.168399 1.313936 0.109695 0.5 True 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.586346 0.576987 0.579852 0.581062 0.003915 19 5 2.339322 0.092595 1.209515 0.132825 0.5 True 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.585634 0.576217 0.580320 0.580723 0.003855 20 20 2.644488 0.119967 1.238554 0.048871 1 False 0.05 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.581096 0.572833 0.576280 0.576736 0.003389 21 8 2.456030 0.072136 1.240463 0.092383 0.5 False 0.05 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.580841 0.572391 0.575731 0.576321 0.003475 22 23 2.335796 0.082338 0.786187 0.073499 1 False 0.1 100 {'classifier__alpha': 1, 'classifier__fit_prio... 0.579769 0.572373 0.576699 0.576280 0.003034 23 11 2.530331 0.107199 1.247842 0.078229 0.5 False 0.1 100 {'classifier__alpha': 0.5, 'classifier__fit_pr... 0.579743 0.571607 0.576352 0.575901 0.003337 24 In [5]: Copied! from sklearn import set_config set_config ( display = \"diagram\" ) classifier = create_pipeline () classifier . set_params ( ** optimisation_pipeline . best_params_ ) classifier . fit ( X_train , y_train ) from sklearn import set_config set_config(display=\"diagram\") classifier = create_pipeline() classifier.set_params(**optimisation_pipeline.best_params_) classifier.fit(X_train, y_train) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('vectorizer', TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True)), ('classifier', MultinomialNB(alpha=1))]) TfidfVectorizer TfidfVectorizer(max_df=0.05, min_df=5, sublinear_tf=True) MultinomialNB MultinomialNB(alpha=1) In [12]: Copied! from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier . predict ( X_test ) % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 20 , 18 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 22 plt . rcParams [ \"axes.xmargin\" ] = 0 print ( metrics . classification_report ( y_test , y_predicted , digits = 4 )) metrics . ConfusionMatrixDisplay . from_predictions ( y_true = y_test , y_pred = y_predicted , xticks_rotation = \"vertical\" , normalize = \"pred\" , values_format = \".2f\" , ) plt . tight_layout () plt . savefig ( \"mag-confusion.png\" , dpi = 600 ) from sklearn import metrics import matplotlib.pyplot as plt y_predicted = classifier.predict(X_test) %matplotlib inline plt.rcParams[\"figure.figsize\"] = (20, 18) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 22 plt.rcParams[\"axes.xmargin\"] = 0 print(metrics.classification_report(y_test, y_predicted, digits=4)) metrics.ConfusionMatrixDisplay.from_predictions( y_true=y_test, y_pred=y_predicted, xticks_rotation=\"vertical\", normalize=\"pred\", values_format=\".2f\", ) plt.tight_layout() plt.savefig(\"mag-confusion.png\", dpi=600) precision recall f1-score support business 0.6412 0.7258 0.6808 3198 economics 0.6635 0.7146 0.6881 3189 geography 0.7461 0.6791 0.7111 3207 medicine 0.8806 0.9021 0.8912 3187 politics 0.5563 0.5895 0.5724 3169 psychology 0.7654 0.6811 0.7208 3252 sociology 0.5165 0.4698 0.4921 3197 accuracy 0.6803 22399 macro avg 0.6814 0.6803 0.6795 22399 weighted avg 0.6817 0.6803 0.6796 22399","title":"Train a field of study (domain) classification of sentences on the MAG dataset"},{"location":"how-to-guides/call-remote/","text":"How to call remote GreatAI instances # Microservices architecture (or SOA ) work well with ML applications. This is because their interfaces are usually very narrow, while the functionality provided quite comprehensive. Hence, drawing the boundaries of responsibilities is more straightforward in the case of ML services than in the case of more traditional business applications. For this reason, it is common to have a tree of models (preferably wrapped in GreatAI instances) communicating with each other. Although regular HTTP POST requests could be sent to each service's /predict endpoint, great-ai comes with two convenience functions: call_remote_great_ai and call_remote_great_ai_async to wrap this request. These provide you with some level of robustness and deserialisation. Inside notebooks The async variant, call_remote_great_ai_async , requires a running event loop while the synchronous variant disallows other running event-loops. Therefore, when running inside a Jupyter Notebook, always call call_remote_great_ai_async . Simple example # Let's create two processes: a server and a client. Server # server.py from great_ai import GreatAI from asyncio import sleep @GreatAI . create async def slow_greeter ( your_name ): await sleep ( 2 ) return f 'Hi { your_name } !' Run this in development mode by executing great-ai server.py or python3 -m great_ai server.py if you're on Windows and great-ai is not in your PATH . Client # client.py from great_ai import call_remote_great_ai names = [ 'Oliv\u00e9r' , 'Bal\u00e1zs' , 'Andr\u00e1s' ] results = [ call_remote_great_ai ( 'http://localhost:6060' , { 'your_name' : name } ) . output #(1) for name in names ] print ( results ) Only return the outputs so we don't clutter up the terminal. Run this script as a regular Python script by executing python3 client.py . As you can see, everything worked as expected. There is one way to improve it though. An async example # Let's send multiple requests at the same time to speed up the overall execution time. To do this, we will use the call_remote_great_ai_async function. Why is this possible? Note, that in server.py , the inference function is declared async . This means that multiple \"copies\" of it can run at the same time in the same thread. Since, there is no CPU bottleneck, the server has a quite large throughput (requests responded to per second), but its latency will stay around 2 seconds due to the async sleep command. If your great-ai server is not async , higher throughput can be achieved by running multiple instances of it, either manually, or by running it with multiple uvicorn workers like this: ENVIRONMENT=production great-ai server.py --worker_count 4 Async client # async-client.py from great_ai import call_remote_great_ai_async import asyncio names = [ 'Oliv\u00e9r' , 'Bal\u00e1zs' , 'Andr\u00e1s' ] async def main (): futures = [ call_remote_great_ai_async ( 'http://localhost:6060' , { 'your_name' : name } ) for name in names ] results = await asyncio . gather ( * futures ) print ([ r . output for r in results ]) asyncio . run ( main ()) Replace client.py with this async client. Note that even though async support is significantly more streamlined in recent Python versions, it still requires a bit more boilerplate than its synchronous counterpart. This also works, and in some use-cases might be considerably quicker.","title":"How to call remote GreatAI instances"},{"location":"how-to-guides/call-remote/#how-to-call-remote-greatai-instances","text":"Microservices architecture (or SOA ) work well with ML applications. This is because their interfaces are usually very narrow, while the functionality provided quite comprehensive. Hence, drawing the boundaries of responsibilities is more straightforward in the case of ML services than in the case of more traditional business applications. For this reason, it is common to have a tree of models (preferably wrapped in GreatAI instances) communicating with each other. Although regular HTTP POST requests could be sent to each service's /predict endpoint, great-ai comes with two convenience functions: call_remote_great_ai and call_remote_great_ai_async to wrap this request. These provide you with some level of robustness and deserialisation. Inside notebooks The async variant, call_remote_great_ai_async , requires a running event loop while the synchronous variant disallows other running event-loops. Therefore, when running inside a Jupyter Notebook, always call call_remote_great_ai_async .","title":"How to call remote GreatAI instances"},{"location":"how-to-guides/call-remote/#simple-example","text":"Let's create two processes: a server and a client.","title":"Simple example"},{"location":"how-to-guides/call-remote/#server","text":"server.py from great_ai import GreatAI from asyncio import sleep @GreatAI . create async def slow_greeter ( your_name ): await sleep ( 2 ) return f 'Hi { your_name } !' Run this in development mode by executing great-ai server.py or python3 -m great_ai server.py if you're on Windows and great-ai is not in your PATH .","title":"Server"},{"location":"how-to-guides/call-remote/#client","text":"client.py from great_ai import call_remote_great_ai names = [ 'Oliv\u00e9r' , 'Bal\u00e1zs' , 'Andr\u00e1s' ] results = [ call_remote_great_ai ( 'http://localhost:6060' , { 'your_name' : name } ) . output #(1) for name in names ] print ( results ) Only return the outputs so we don't clutter up the terminal. Run this script as a regular Python script by executing python3 client.py . As you can see, everything worked as expected. There is one way to improve it though.","title":"Client"},{"location":"how-to-guides/call-remote/#an-async-example","text":"Let's send multiple requests at the same time to speed up the overall execution time. To do this, we will use the call_remote_great_ai_async function. Why is this possible? Note, that in server.py , the inference function is declared async . This means that multiple \"copies\" of it can run at the same time in the same thread. Since, there is no CPU bottleneck, the server has a quite large throughput (requests responded to per second), but its latency will stay around 2 seconds due to the async sleep command. If your great-ai server is not async , higher throughput can be achieved by running multiple instances of it, either manually, or by running it with multiple uvicorn workers like this: ENVIRONMENT=production great-ai server.py --worker_count 4","title":"An async example"},{"location":"how-to-guides/call-remote/#async-client","text":"async-client.py from great_ai import call_remote_great_ai_async import asyncio names = [ 'Oliv\u00e9r' , 'Bal\u00e1zs' , 'Andr\u00e1s' ] async def main (): futures = [ call_remote_great_ai_async ( 'http://localhost:6060' , { 'your_name' : name } ) for name in names ] results = await asyncio . gather ( * futures ) print ([ r . output for r in results ]) asyncio . run ( main ()) Replace client.py with this async client. Note that even though async support is significantly more streamlined in recent Python versions, it still requires a bit more boilerplate than its synchronous counterpart. This also works, and in some use-cases might be considerably quicker.","title":"Async client"},{"location":"how-to-guides/configure-service/","text":"How to configure GreatAI # GreatAI aims to provide reasonable defaults wherever possible. The current configuration is always prominently displayed (and updated) on the dashboard and in the command-line start-up banner. Using great_ai.configure # You can override any of the default settings by calling great_ai.configure . If you don't call configure , the default settings are applied on the first call to most great-ai functions. Warning You must call great_ai.configure before calling (or decorating with) any other great-ai function. However, importing other functions before calling great_ai.configure is permitted. configure-demo.py from great_ai import configure , RouteConfig import logging configure ( version = '1.0.0' , log_level = logging . INFO , seed = 2 , should_log_exception_stack = False , prediction_cache_size = 0 , #(1) disable_se4ml_banner = True , dashboard_table_size = 200 , route_config = RouteConfig ( #(2) feedback_endpoints_enabled = False , dashboard_enabled = False ) ) Completely disable caching. The unspecified routes are enabled by default. Using remote storage # The only aspect that cannot be automated is choosing the backing storage for the database and file storage. Right now, you have 3 options for storing the models and large datasets: LargeFileLocal , LargeFileMongo , and LargeFileS3 . Without explicit configuration, LargeFileLocal is selected by default. This one still version-controls your files but it only stores them in a local path (which of course can be a remote volume attached by NFS , HDFS , etc.). Important If your working directory contains a mongo.ini or s3.ini file, an attempt is made to auto-configure LargeFileMongo or LargeFileS3 respectively. To use LargeFileMongo or LargeFileS3 explicitly, configure them before calling any other great-ai function. S3-compatible # s3.ini aws_region_name = eu-west-2 aws_access_key_id = MY_AWS_ACCESS_KEY # ENV:MY_AWS_ACCESS_KEY would also work aws_secret_access_key = MY_AWS_SECRET_KEY large_files_bucket_name = bucket-for-models use-s3.py from great_ai.large_file import LargeFileS3 from great_ai import save_model LargeFileS3 . configure_credentials_from_file ( 's3.ini' ) #(1) model = [ 4 , 3 ] save_model ( model , 'my-model' ) This line isn't strictly necessary because if s3.ini (or mongo.ini ) is available in the current working directory, they are automatically used to configure their respective LargeFile implementations/databases. Departing from AWS With the aws_endpoint_url argument, it is possible to use any other S3-compatible service such as Backblaze . In that case, it would be aws_endpoint_url=https://s3.us-west-002.backblazeb2.com . GridFS # GridFS specifies how to store files in MongoDB. The official MongoDB server and many compatible implementations support it. mongo.ini MONGO_CONNECTION_STRING = mongodb : // localhost : 27017 # this is the default value # if `MONGO_CONNECTION_STRING` is specified, this default is overridden MONGO_CONNECTION_STRING = ENV : MONGO_CONNECTION_STRING MONGO_DATABASE = my-database # it is automatically created if doesn't exist use-mongo.py from great_ai.large_file import LargeFileMongo from great_ai import save_model LargeFileMongo . configure_credentials_from_file ( 'mongo.ini' ) model = [ 4 , 3 ] save_model ( model , 'my-model' ) Simplifying config files You can combine mongo.ini or s3.ini with your application's config file because the unneeded keys are ignored by the configure_credentials_from_file method. Using a database # By default, a thread-safe version of TinyDB is utilised for saving the prediction traces into a local file. Unfortunately, for most production needs, this method is not suitable. MongoDB # At the moment, only MongoDB is supported as a production-ready TracingDatabase . In order to use it, you have to either place a file named mongo.ini in your working directory, or explicitly call either MongoDbDriver.configure_credentials_from_file or MongoDbDriver.configure_credentials .","title":"How to configure GreatAI"},{"location":"how-to-guides/configure-service/#how-to-configure-greatai","text":"GreatAI aims to provide reasonable defaults wherever possible. The current configuration is always prominently displayed (and updated) on the dashboard and in the command-line start-up banner.","title":"How to configure GreatAI"},{"location":"how-to-guides/configure-service/#using-great_aiconfigure","text":"You can override any of the default settings by calling great_ai.configure . If you don't call configure , the default settings are applied on the first call to most great-ai functions. Warning You must call great_ai.configure before calling (or decorating with) any other great-ai function. However, importing other functions before calling great_ai.configure is permitted. configure-demo.py from great_ai import configure , RouteConfig import logging configure ( version = '1.0.0' , log_level = logging . INFO , seed = 2 , should_log_exception_stack = False , prediction_cache_size = 0 , #(1) disable_se4ml_banner = True , dashboard_table_size = 200 , route_config = RouteConfig ( #(2) feedback_endpoints_enabled = False , dashboard_enabled = False ) ) Completely disable caching. The unspecified routes are enabled by default.","title":"Using great_ai.configure"},{"location":"how-to-guides/configure-service/#using-remote-storage","text":"The only aspect that cannot be automated is choosing the backing storage for the database and file storage. Right now, you have 3 options for storing the models and large datasets: LargeFileLocal , LargeFileMongo , and LargeFileS3 . Without explicit configuration, LargeFileLocal is selected by default. This one still version-controls your files but it only stores them in a local path (which of course can be a remote volume attached by NFS , HDFS , etc.). Important If your working directory contains a mongo.ini or s3.ini file, an attempt is made to auto-configure LargeFileMongo or LargeFileS3 respectively. To use LargeFileMongo or LargeFileS3 explicitly, configure them before calling any other great-ai function.","title":"Using remote storage"},{"location":"how-to-guides/configure-service/#s3-compatible","text":"s3.ini aws_region_name = eu-west-2 aws_access_key_id = MY_AWS_ACCESS_KEY # ENV:MY_AWS_ACCESS_KEY would also work aws_secret_access_key = MY_AWS_SECRET_KEY large_files_bucket_name = bucket-for-models use-s3.py from great_ai.large_file import LargeFileS3 from great_ai import save_model LargeFileS3 . configure_credentials_from_file ( 's3.ini' ) #(1) model = [ 4 , 3 ] save_model ( model , 'my-model' ) This line isn't strictly necessary because if s3.ini (or mongo.ini ) is available in the current working directory, they are automatically used to configure their respective LargeFile implementations/databases. Departing from AWS With the aws_endpoint_url argument, it is possible to use any other S3-compatible service such as Backblaze . In that case, it would be aws_endpoint_url=https://s3.us-west-002.backblazeb2.com .","title":"S3-compatible"},{"location":"how-to-guides/configure-service/#gridfs","text":"GridFS specifies how to store files in MongoDB. The official MongoDB server and many compatible implementations support it. mongo.ini MONGO_CONNECTION_STRING = mongodb : // localhost : 27017 # this is the default value # if `MONGO_CONNECTION_STRING` is specified, this default is overridden MONGO_CONNECTION_STRING = ENV : MONGO_CONNECTION_STRING MONGO_DATABASE = my-database # it is automatically created if doesn't exist use-mongo.py from great_ai.large_file import LargeFileMongo from great_ai import save_model LargeFileMongo . configure_credentials_from_file ( 'mongo.ini' ) model = [ 4 , 3 ] save_model ( model , 'my-model' ) Simplifying config files You can combine mongo.ini or s3.ini with your application's config file because the unneeded keys are ignored by the configure_credentials_from_file method.","title":"GridFS"},{"location":"how-to-guides/configure-service/#using-a-database","text":"By default, a thread-safe version of TinyDB is utilised for saving the prediction traces into a local file. Unfortunately, for most production needs, this method is not suitable.","title":"Using a database"},{"location":"how-to-guides/configure-service/#mongodb","text":"At the moment, only MongoDB is supported as a production-ready TracingDatabase . In order to use it, you have to either place a file named mongo.ini in your working directory, or explicitly call either MongoDbDriver.configure_credentials_from_file or MongoDbDriver.configure_credentials .","title":"MongoDB"},{"location":"how-to-guides/create-service/","text":"How to create a GreatAI service # The core value of great-ai lies in its GreatAI class. In order to take advantage of it, you need to create an instance wrapping your code. Let's say that you have the following greeter function: greeter.py def my_greeter_function ( your_name ): return f 'Hi { your_name } !' You can simply decorate (wrap) this function using the @GreatAI.create factory. greeter.py from great_ai import GreatAI @GreatAI . create def greeter ( your_name ): return f 'Hi { your_name } !' Why not simply use @GreatAI? The purpose of @GreatAI.create is simply to provide you with type-checking through MyPy, Pylance, and similar libraries. However, the overloading support for __new__ is lacking in MyPy, thus, a static factory method is used instead. With types # Even though it's not required by GreatAI, type annotating your codebase can save you from lots of trivial mistakes, that's why it's highly advised. Simply add the expected types to your function's signature. type_safe_greeter.py from great_ai import GreatAI @GreatAI . create def type_safe_greeter ( your_name : str ) -> str : return f 'Hi { your_name } !' This not only allows you to statically typecheck your code, but by default, GreatAI will check it during runtime as well using typeguard . With async # Asynchronous code can result in immense performance gains in certain cases. For example, you might rely on a third-party service, do database access, or call a remote GreatAI instance . In these cases, you can simply make your function async without any other changes. async_greeter.py from great_ai import GreatAI from asyncio import sleep @GreatAI . create async def async_greeter ( your_name : str ) -> str : await sleep ( 2 ) # simulate IO-bound operation return f 'Hi { your_name } !' With decorators # GreatAI can decorate already decorated functions. The only restriction is that @GreatAI.create must come last. There are two built-in decorators that you can use to customise your function but you can you use any third-party decorator as well. Using @use_model # If you have previously saved a model with save_model , you can inject it into your function by calling @use_model . greeter_with_model.py from great_ai import GreatAI , use_model @GreatAI . create @use_model ( 'name_of_my_model' , version = 'latest' ) #(1) def type_safe_greeter ( your_name : str , model ) -> str : return f 'Hi { your_name } !' assert type_safe_greeter ( 'Andras' ) . output == 'Hi Andras' By default, the parameter named model will be replaced by the loaded model. This behaviour can be customised by setting the model_kwarg_name . This way, even multiple models can be injected into a single function. Important You must call @use_model before @GreatAI.create . Note, that decorators are applied starting from the bottom-most one. Feel free to use @use_model in other places of the codebase, it works equally well outside of GreatAI services. Using @parameter # If you wish to turn off logging or specify custom validation for your parameters, you can use the @parameter decorator. Note By default, all parameters that are not affected by an explicit @parameter or @use_model are automatically decorated with @parameter when @GreatAI.create is called. greeter_with_validation.py from great_ai import GreatAI , use_model @GreatAI . create @parameter ( 'your_name' , disable_logging = True ) def type_safe_greeter ( your_name : str , model ) -> str : return f 'Hi { your_name } !' assert type_safe_greeter ( 'Andras' ) . output == 'Hi Andras' Important You must call @parameter before @GreatAI.create . Note, that decorators are applied starting from the bottom-most one. Feel free to use @parameter in other places of the codebase, it works equally well outside of GreatAI services. Complex example # Refer to the following example summarising the options you have when instantiating a GreatAI service. complex.py from great_ai import save_model , GreatAI , parameter , use_model , log_metric save_model ( 4 , 'secret-number' ) #(1) @GreatAI . create @parameter ( 'positive_number' , validate = lambda n : n > 0 , disable_logging = True ) @use_model ( 'secret-number' , version = 'latest' , model_kwarg_name = 'secret' ) def add_number ( positive_number : int , secret : int ) -> int : log_metric ( 'log directly into the returned Trace' , positive_number * 2 ) return positive_number + secret assert add_number ( 1 ) . output == 5 Refer to the configuration page for specifying where to store your models.","title":"How to create a GreatAI service"},{"location":"how-to-guides/create-service/#how-to-create-a-greatai-service","text":"The core value of great-ai lies in its GreatAI class. In order to take advantage of it, you need to create an instance wrapping your code. Let's say that you have the following greeter function: greeter.py def my_greeter_function ( your_name ): return f 'Hi { your_name } !' You can simply decorate (wrap) this function using the @GreatAI.create factory. greeter.py from great_ai import GreatAI @GreatAI . create def greeter ( your_name ): return f 'Hi { your_name } !' Why not simply use @GreatAI? The purpose of @GreatAI.create is simply to provide you with type-checking through MyPy, Pylance, and similar libraries. However, the overloading support for __new__ is lacking in MyPy, thus, a static factory method is used instead.","title":"How to create a GreatAI service"},{"location":"how-to-guides/create-service/#with-types","text":"Even though it's not required by GreatAI, type annotating your codebase can save you from lots of trivial mistakes, that's why it's highly advised. Simply add the expected types to your function's signature. type_safe_greeter.py from great_ai import GreatAI @GreatAI . create def type_safe_greeter ( your_name : str ) -> str : return f 'Hi { your_name } !' This not only allows you to statically typecheck your code, but by default, GreatAI will check it during runtime as well using typeguard .","title":"With types"},{"location":"how-to-guides/create-service/#with-async","text":"Asynchronous code can result in immense performance gains in certain cases. For example, you might rely on a third-party service, do database access, or call a remote GreatAI instance . In these cases, you can simply make your function async without any other changes. async_greeter.py from great_ai import GreatAI from asyncio import sleep @GreatAI . create async def async_greeter ( your_name : str ) -> str : await sleep ( 2 ) # simulate IO-bound operation return f 'Hi { your_name } !'","title":"With async"},{"location":"how-to-guides/create-service/#with-decorators","text":"GreatAI can decorate already decorated functions. The only restriction is that @GreatAI.create must come last. There are two built-in decorators that you can use to customise your function but you can you use any third-party decorator as well.","title":"With decorators"},{"location":"how-to-guides/create-service/#using-use_model","text":"If you have previously saved a model with save_model , you can inject it into your function by calling @use_model . greeter_with_model.py from great_ai import GreatAI , use_model @GreatAI . create @use_model ( 'name_of_my_model' , version = 'latest' ) #(1) def type_safe_greeter ( your_name : str , model ) -> str : return f 'Hi { your_name } !' assert type_safe_greeter ( 'Andras' ) . output == 'Hi Andras' By default, the parameter named model will be replaced by the loaded model. This behaviour can be customised by setting the model_kwarg_name . This way, even multiple models can be injected into a single function. Important You must call @use_model before @GreatAI.create . Note, that decorators are applied starting from the bottom-most one. Feel free to use @use_model in other places of the codebase, it works equally well outside of GreatAI services.","title":"Using @use_model"},{"location":"how-to-guides/create-service/#using-parameter","text":"If you wish to turn off logging or specify custom validation for your parameters, you can use the @parameter decorator. Note By default, all parameters that are not affected by an explicit @parameter or @use_model are automatically decorated with @parameter when @GreatAI.create is called. greeter_with_validation.py from great_ai import GreatAI , use_model @GreatAI . create @parameter ( 'your_name' , disable_logging = True ) def type_safe_greeter ( your_name : str , model ) -> str : return f 'Hi { your_name } !' assert type_safe_greeter ( 'Andras' ) . output == 'Hi Andras' Important You must call @parameter before @GreatAI.create . Note, that decorators are applied starting from the bottom-most one. Feel free to use @parameter in other places of the codebase, it works equally well outside of GreatAI services.","title":"Using @parameter"},{"location":"how-to-guides/create-service/#complex-example","text":"Refer to the following example summarising the options you have when instantiating a GreatAI service. complex.py from great_ai import save_model , GreatAI , parameter , use_model , log_metric save_model ( 4 , 'secret-number' ) #(1) @GreatAI . create @parameter ( 'positive_number' , validate = lambda n : n > 0 , disable_logging = True ) @use_model ( 'secret-number' , version = 'latest' , model_kwarg_name = 'secret' ) def add_number ( positive_number : int , secret : int ) -> int : log_metric ( 'log directly into the returned Trace' , positive_number * 2 ) return positive_number + secret assert add_number ( 1 ) . output == 5 Refer to the configuration page for specifying where to store your models.","title":"Complex example"},{"location":"how-to-guides/handle-training-data/","text":"How to manage training data # In order to simplify your training data management, great-ai provide two complementing approaches for inputting new data-points. Upload data # At the start of your experiments' first iteration, after you've gathered suitable samples for training, you can call great_ai.add_ground_truth . This automatically stores a timestamp and also allows you to assign tags to the data. Using these attributes, great_ai.query_ground_truth can be called to get a filtered view of the training data. Train-test-validation splits It is a best-practice to lock-away the test split of your data that is only used for the final quality assessment. This prevents you from accidentally training on it, or inadvertently tuning the model to have the highest accuracy metrics on the test split. This, of course, may lead to dubious results, hence, care must be taken to avoid it. With great_ai.add_ground_truth , there is an option to tag the samples with train , test , and validation randomly, following a predefined distribution. This happens as soon as they're written in the database. Later, these can be queried by providing the name of the appropriate tags. The nice thing about this is that the 'input-expected output' pairs are stored as traces. Thus, they behave exactly like regular prediction traces. from great_ai import add_ground_truth add_ground_truth ( [ 1 , 2 ], [ 'odd' , 'even' ], tags = 'my_tag' , train_split_ratio = 1 , #(1) test_split_ratio = 1 ) Note that the ratios don't have to add up to 1. They are just weights. There is also a validation_split_ratio which is 0 by default. >>> from great_ai import query_ground_truth >>> query_ground_truth ( 'my_tag' ) [ Trace [ str ]({ 'created' : '2022-07-12T18:36:12.825706' , 'exception' : None , 'feedback' : 'odd' , #(1) 'logged_values' : { 'input' : 1 }, #(2) 'models' : [], 'original_execution_time_ms' : 0.0 , 'output' : 'odd' , 'tags' : [ 'ground_truth' , 'test' , 'my_tag' ], #(3) 'trace_id' : '4fcf2ce6-a172-469d-94b2-874577655814' }), Trace [ str ]({ 'created' : '2022-07-12T18:36:12.825706' , 'exception' : None , 'feedback' : 'even' , 'logged_values' : { 'input' : 2 }, 'models' : [], 'original_execution_time_ms' : 0.0 , 'output' : 'even' , 'tags' : [ 'ground_truth' , 'train' , 'my_tag' ], 'trace_id' : 'abee0671-beb9-4284-8c3b-c65e5836ce38' })] Expected output. This can be also accessed through the .output property. The input value is stored here. Notice how ground_truth is always included as a tag when using great_ai.add_ground_truth . Get feedback # After the initial data gathering, end-to-end feedback can be also integrated into the dataset. The scaffolded REST API contains endpoints for managing traces and their feedbacks. When great_ai.query_ground_truth is executed, it implicitly filters for traces that have feedback. Therefore, both the ground_truth and the online traces that have received feedback are returned. No matter the origin of the data, it can be accessed using the same API. Remove clutter # Traces can be deleted either through the REST API or by calling great_ai.delete_ground_truth . The latter provides the same interface as great_ai.query_ground_truth except it deletes the matched points.","title":"How to manage training data"},{"location":"how-to-guides/handle-training-data/#how-to-manage-training-data","text":"In order to simplify your training data management, great-ai provide two complementing approaches for inputting new data-points.","title":"How to manage training data"},{"location":"how-to-guides/handle-training-data/#upload-data","text":"At the start of your experiments' first iteration, after you've gathered suitable samples for training, you can call great_ai.add_ground_truth . This automatically stores a timestamp and also allows you to assign tags to the data. Using these attributes, great_ai.query_ground_truth can be called to get a filtered view of the training data. Train-test-validation splits It is a best-practice to lock-away the test split of your data that is only used for the final quality assessment. This prevents you from accidentally training on it, or inadvertently tuning the model to have the highest accuracy metrics on the test split. This, of course, may lead to dubious results, hence, care must be taken to avoid it. With great_ai.add_ground_truth , there is an option to tag the samples with train , test , and validation randomly, following a predefined distribution. This happens as soon as they're written in the database. Later, these can be queried by providing the name of the appropriate tags. The nice thing about this is that the 'input-expected output' pairs are stored as traces. Thus, they behave exactly like regular prediction traces. from great_ai import add_ground_truth add_ground_truth ( [ 1 , 2 ], [ 'odd' , 'even' ], tags = 'my_tag' , train_split_ratio = 1 , #(1) test_split_ratio = 1 ) Note that the ratios don't have to add up to 1. They are just weights. There is also a validation_split_ratio which is 0 by default. >>> from great_ai import query_ground_truth >>> query_ground_truth ( 'my_tag' ) [ Trace [ str ]({ 'created' : '2022-07-12T18:36:12.825706' , 'exception' : None , 'feedback' : 'odd' , #(1) 'logged_values' : { 'input' : 1 }, #(2) 'models' : [], 'original_execution_time_ms' : 0.0 , 'output' : 'odd' , 'tags' : [ 'ground_truth' , 'test' , 'my_tag' ], #(3) 'trace_id' : '4fcf2ce6-a172-469d-94b2-874577655814' }), Trace [ str ]({ 'created' : '2022-07-12T18:36:12.825706' , 'exception' : None , 'feedback' : 'even' , 'logged_values' : { 'input' : 2 }, 'models' : [], 'original_execution_time_ms' : 0.0 , 'output' : 'even' , 'tags' : [ 'ground_truth' , 'train' , 'my_tag' ], 'trace_id' : 'abee0671-beb9-4284-8c3b-c65e5836ce38' })] Expected output. This can be also accessed through the .output property. The input value is stored here. Notice how ground_truth is always included as a tag when using great_ai.add_ground_truth .","title":"Upload data"},{"location":"how-to-guides/handle-training-data/#get-feedback","text":"After the initial data gathering, end-to-end feedback can be also integrated into the dataset. The scaffolded REST API contains endpoints for managing traces and their feedbacks. When great_ai.query_ground_truth is executed, it implicitly filters for traces that have feedback. Therefore, both the ground_truth and the online traces that have received feedback are returned. No matter the origin of the data, it can be accessed using the same API.","title":"Get feedback"},{"location":"how-to-guides/handle-training-data/#remove-clutter","text":"Traces can be deleted either through the REST API or by calling great_ai.delete_ground_truth . The latter provides the same interface as great_ai.query_ground_truth except it deletes the matched points.","title":"Remove clutter"},{"location":"how-to-guides/install/","text":"Installation guide # Provided you already have Python3 (and pip) installed, simply execute: pip install great-ai Python 3.8 or later is required. This will work on all major operating systems. Command-line tools # After installation, great-ai and large-file are available as commands. The former is required for deploying your application while the latter lets you manage models and datasets from your terminal. Snakes & kebabs The library is called great-ai , therefore, its command-line entrypoint is also called great-ai . However, Python module names cannot contain hyphens, that's why you have to import great_ai with an underscore. The great-ai CLI tool is also available as python3 -m great_ai . To help with the confusion, a CLI executable called great_ai (and large_file ) are also installed, thus, if you prefer, you can always refer to GreatAI using its underscored name variant ( great_ai ). Windows On Windows, you might encounter a similar warning from pip : WARNING: The scripts great-ai.exe, great_ai.exe, large-file.exe and large_file.exe are installed in 'C:\\Users\\...\\Scripts' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. This means that great-ai.exe and large-file.exe are not in your PATH . Either add their containing directory ('C:\\Users...\\Scripts' in this case) to your PATH or use python3 -m great_ai and python3 -m great_ai.large_file instead of the exe-s. Update # If you wish to update to the latest version execute: pip install --upgrade great-ai","title":"Installation guide"},{"location":"how-to-guides/install/#installation-guide","text":"Provided you already have Python3 (and pip) installed, simply execute: pip install great-ai Python 3.8 or later is required. This will work on all major operating systems.","title":"Installation guide"},{"location":"how-to-guides/install/#command-line-tools","text":"After installation, great-ai and large-file are available as commands. The former is required for deploying your application while the latter lets you manage models and datasets from your terminal. Snakes & kebabs The library is called great-ai , therefore, its command-line entrypoint is also called great-ai . However, Python module names cannot contain hyphens, that's why you have to import great_ai with an underscore. The great-ai CLI tool is also available as python3 -m great_ai . To help with the confusion, a CLI executable called great_ai (and large_file ) are also installed, thus, if you prefer, you can always refer to GreatAI using its underscored name variant ( great_ai ). Windows On Windows, you might encounter a similar warning from pip : WARNING: The scripts great-ai.exe, great_ai.exe, large-file.exe and large_file.exe are installed in 'C:\\Users\\...\\Scripts' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. This means that great-ai.exe and large-file.exe are not in your PATH . Either add their containing directory ('C:\\Users...\\Scripts' in this case) to your PATH or use python3 -m great_ai and python3 -m great_ai.large_file instead of the exe-s.","title":"Command-line tools"},{"location":"how-to-guides/install/#update","text":"If you wish to update to the latest version execute: pip install --upgrade great-ai","title":"Update"},{"location":"how-to-guides/large_file/","text":"How to use LargeFiles # The functions save_model and @use_model wrap LargeFile instances. Hence, besides configuring LargeFile , users have few reasons to use LargeFiles directly. Motivation # Oftentimes, especially when working with data-heavy applications, large files can proliferate in a repository. Version controlling them is an obvious next step, however, GitHub's git LFS implementation doesn't support deleting large files, making it easy for them to eat-up the LFS quota and explode the size of your repos. DVC is a viable alternative, however, it requires users to learn to use one more CLI tool. Using LargeFile-s directly (usually not needed) LargeFile doesn't require users to learn too much new. It is a nearly exact copy of the built-in open() function of Python with which users are certainly already familiar. Simple example # from great_ai.large_file import LargeFileS3 LargeFileS3 . configure_credentials ({ \"aws_region_name\" : \"your_region_like_eu-west-2\" , \"aws_access_key_id\" : \"YOUR_ACCESS_KEY_ID\" , \"aws_secret_access_key\" : \"YOUR_VERY_SECRET_ACCESS_KEY\" , \"large_files_bucket_name\" : \"create_a_bucket_and_put_its_name_here\" , }) # Creates a new version and deletes the older version # leaving the 3 most recently used intact with LargeFileS3 ( \"test.txt\" , \"w\" , keep_last_n = 3 ) as f : for i in range ( 100000 ): f . write ( 'test \\n ' ) # The latest version is returned by default # but an optional `version` keyword argument can be provided as well with LargeFileS3 ( \"test.txt\" , \"r\" ) as f : #(1) print ( f . readlines ()[ 0 ]) In this case, the latest version is already in the local cache, no download is required. More details # LargeFile behaves like an opened file (in the background it is a temp file after all). Binary reads and writes are supported along with the different keywords open() accepts . The local cache can be configured with these properties: LargeFileS3 . cache_path = Path ( '.cache' ) LargeFileS3 . max_cache_size = \"30 GB\" I only need a path # In case you only need a path to the (proxy of the) remote file, this pattern can be applied: path_to_model = LargeFileS3 ( \"folder-of-my-bert-model\" , version = 31 ) . get () This will first download the file/folder into your local cache folder. Then, it returns a Path object to the local version. Which can be turned into a string with str(path_to_model) . The same approach works for uploads: LargeFileS3 ( \"folder-of-my-bert-model\" ) . push ( 'path_to_local/folder_or_file' ) This way both regular files and folders can be handled. The uploaded file is called folder-of-my-bert-model , the local name is ignored. Lastly, all version of the remote object can be deleted by calling LargeFileS3(\"my-file\").delete() . It will still reside in your local cache afterwards; its deletion will happen next time your local cache has to be pruned. From the command-line # The main reason for using the large-file or python3 -m great_ai.large_file commands is to upload or download models from the terminal. For example, when building a docker image, it is best-practice to cache the referred models. Setup # Create an .ini file (or use ~/.aws/credentials ). It may look like this: aws_region_name = your_region_like_eu-west-2 aws_access_key_id = YOUR_ACCESS_KEY_ID aws_secret_access_key = YOUR_VERY_SECRET_ACCESS_KEY large_files_bucket_name = my_large_files Upload some files # large-file --backend s3 --secrets secrets.ini \\ --push my_first_file.json folder/my_second_file my_folder Only the filename is used as the S3 name, the rest of the path is ignored. Using MongoDB The possible values for --backend are s3 , mongo , and local . The latter doesn't need credentials, it only versions and stores your files in a local folder. MongoDB on the other hand requires a mongo_connection_string and a mongo_database to be specified. For storing large files, it uses the GridFS specification. Download some files to the local cache # This can be useful when building a Docker image for example. This way, the files can already reside inside the container and need not be downloaded later. large-file --backend s3 --secrets ~/.aws/credentials \\ --cache my_first_file.json:3 my_second_file my_folder:0 Versions may be specified by using : -s. Delete remote files # large-file --backend s3 --secrets ~/.aws/credentials \\ --delete my_first_file.json","title":"How to use LargeFiles"},{"location":"how-to-guides/large_file/#how-to-use-largefiles","text":"The functions save_model and @use_model wrap LargeFile instances. Hence, besides configuring LargeFile , users have few reasons to use LargeFiles directly.","title":"How to use LargeFiles"},{"location":"how-to-guides/large_file/#motivation","text":"Oftentimes, especially when working with data-heavy applications, large files can proliferate in a repository. Version controlling them is an obvious next step, however, GitHub's git LFS implementation doesn't support deleting large files, making it easy for them to eat-up the LFS quota and explode the size of your repos. DVC is a viable alternative, however, it requires users to learn to use one more CLI tool. Using LargeFile-s directly (usually not needed) LargeFile doesn't require users to learn too much new. It is a nearly exact copy of the built-in open() function of Python with which users are certainly already familiar.","title":"Motivation"},{"location":"how-to-guides/large_file/#simple-example","text":"from great_ai.large_file import LargeFileS3 LargeFileS3 . configure_credentials ({ \"aws_region_name\" : \"your_region_like_eu-west-2\" , \"aws_access_key_id\" : \"YOUR_ACCESS_KEY_ID\" , \"aws_secret_access_key\" : \"YOUR_VERY_SECRET_ACCESS_KEY\" , \"large_files_bucket_name\" : \"create_a_bucket_and_put_its_name_here\" , }) # Creates a new version and deletes the older version # leaving the 3 most recently used intact with LargeFileS3 ( \"test.txt\" , \"w\" , keep_last_n = 3 ) as f : for i in range ( 100000 ): f . write ( 'test \\n ' ) # The latest version is returned by default # but an optional `version` keyword argument can be provided as well with LargeFileS3 ( \"test.txt\" , \"r\" ) as f : #(1) print ( f . readlines ()[ 0 ]) In this case, the latest version is already in the local cache, no download is required.","title":"Simple example"},{"location":"how-to-guides/large_file/#more-details","text":"LargeFile behaves like an opened file (in the background it is a temp file after all). Binary reads and writes are supported along with the different keywords open() accepts . The local cache can be configured with these properties: LargeFileS3 . cache_path = Path ( '.cache' ) LargeFileS3 . max_cache_size = \"30 GB\"","title":"More details"},{"location":"how-to-guides/large_file/#i-only-need-a-path","text":"In case you only need a path to the (proxy of the) remote file, this pattern can be applied: path_to_model = LargeFileS3 ( \"folder-of-my-bert-model\" , version = 31 ) . get () This will first download the file/folder into your local cache folder. Then, it returns a Path object to the local version. Which can be turned into a string with str(path_to_model) . The same approach works for uploads: LargeFileS3 ( \"folder-of-my-bert-model\" ) . push ( 'path_to_local/folder_or_file' ) This way both regular files and folders can be handled. The uploaded file is called folder-of-my-bert-model , the local name is ignored. Lastly, all version of the remote object can be deleted by calling LargeFileS3(\"my-file\").delete() . It will still reside in your local cache afterwards; its deletion will happen next time your local cache has to be pruned.","title":"I only need a path"},{"location":"how-to-guides/large_file/#from-the-command-line","text":"The main reason for using the large-file or python3 -m great_ai.large_file commands is to upload or download models from the terminal. For example, when building a docker image, it is best-practice to cache the referred models.","title":"From the command-line"},{"location":"how-to-guides/large_file/#setup","text":"Create an .ini file (or use ~/.aws/credentials ). It may look like this: aws_region_name = your_region_like_eu-west-2 aws_access_key_id = YOUR_ACCESS_KEY_ID aws_secret_access_key = YOUR_VERY_SECRET_ACCESS_KEY large_files_bucket_name = my_large_files","title":"Setup"},{"location":"how-to-guides/large_file/#upload-some-files","text":"large-file --backend s3 --secrets secrets.ini \\ --push my_first_file.json folder/my_second_file my_folder Only the filename is used as the S3 name, the rest of the path is ignored. Using MongoDB The possible values for --backend are s3 , mongo , and local . The latter doesn't need credentials, it only versions and stores your files in a local folder. MongoDB on the other hand requires a mongo_connection_string and a mongo_database to be specified. For storing large files, it uses the GridFS specification.","title":"Upload some files"},{"location":"how-to-guides/large_file/#download-some-files-to-the-local-cache","text":"This can be useful when building a Docker image for example. This way, the files can already reside inside the container and need not be downloaded later. large-file --backend s3 --secrets ~/.aws/credentials \\ --cache my_first_file.json:3 my_second_file my_folder:0 Versions may be specified by using : -s.","title":"Download some files to the local cache"},{"location":"how-to-guides/large_file/#delete-remote-files","text":"large-file --backend s3 --secrets ~/.aws/credentials \\ --delete my_first_file.json","title":"Delete remote files"},{"location":"how-to-guides/use-service/","text":"How to perform prediction with GreatAI # After creating a GreatAI service by wrapping your prediction function, and optionally configuring it , it's time to do some prediction. Let's take the following example: greeter.py from great_ai import GreatAI @GreatAI . create def greeter ( your_name : str ) -> str : return f 'Hi { your_name } !' One-off prediction # Even though greeter is now an instance of GreatAI , you can continue using it as a regular function. >>> greeter ( 'Bob' ) Trace [ str ]({ 'created' : '2022-07-11T14:31:46.183764' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 3 , 'arg:your_name:value' : 'Bob' }, 'models' : [], 'original_execution_time_ms' : 0.0381 , 'output' : 'Hi Bob!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : '7c284fd7-7f0d-4464-b5f8-3ef126df34af' }) As you can see, the original return value is wrapped in a Trace object (which is also persisted in your database of choice). You can access the original value under the output property. Online prediction # Likely, the main way you would like to expose your model is through an HTTP API. @GreatAI.create scaffolds many REST API endpoints for your model and creates a FastAPI app available under GreatAI.app . This can be served using uvicorn or any other ASGI server . Since most ML code lives in Jupyter notebooks, therefore, deploying a notebook containing the inference function is supported. To achieve this, uvicorn is wrapped by the great-ai command-line utility which \u2014 among others \u2014 takes care of feeding a notebook into uvicorn . It also supports auto-reloading. In development # great-ai greeter.py Success Your model is accessible at localhost:6060 . Some configuration options are also supported. great-ai greeter.py --port 8000 --host 127 .0.0.1 --timeout_keep_alive 10 More options For more options (but no Notebook support), simply use uvicorn for starting your app (available at greeter.app ). In production # There are three main approaches for deploying a GreatAI service. Manual deployment # The app is run in production-mode if the value of the ENVIRONMENT environment variable is set to production . ENVIRONMENT = production great-ai greeter.py Simply run ENVIRONMENT=production great-ai deploy.ipynb in the command-line of a production machine. This is the crudest approach; however, it might be fitting for some contexts. Containerised deployment # Run the notebook directly in a container or create a service for it using your favourite container orchestrator. docker run -p 6060 :6060 --volume ` pwd ` :/app --rm \\ schmelczera/great-ai deploy.ipynb You can replace pwd with the path to your code's folder. Use a Platform-as-a-Service # Similar to the previous approach, your code will run in a container. However, instead of manually managing it, you can just choose from a plethora of PaaS providers (such as AWS ECS , DO App platform , MLEM , Streamlit ) that take a Docker image as a source and handle the rest of the deployment. To this end, you can also create a custom Docker image. It is especially useful if you have third-party dependencies, such as PyTorch or TensorFlow . FROM schmelczera/great-ai:latest # Remove this block if you don't have a requirements.txt COPY requirements.txt ./ RUN pip install --no-cache-dir --requirement requirements.txt # If you store your models in S3 or GridFS, it may be a # good idea to cache them in the image so that you don't # have to download it each time a container starts RUN large-file --backend s3 --secrets s3.ini --cache my-domain-predictor # Add you application code to the image COPY . . # The default ENTRYPOINT is great-ai, specify its argument using CMD CMD [ \"deploy.ipynb\" ] Batch prediction # Processing larger amounts of data on a single machine is made easy by the GreatAI 's process_batch method. This relies on multiprocessing ( parallel_map ) to take full advantage of all available CPU-cores. >>> greeter . process_batch ([ 'Alice' , 'Bob' ]) [ Trace [ str ]({ 'created' : '2022-07-11T14:36:37.119183' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 5 , 'arg:your_name:value' : 'Alice' }, 'models' : [], 'original_execution_time_ms' : 0.1251 , 'output' : 'Hi Alice!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : '90ffa15f-e839-41c4-8e7a-3211168bc138' }), Trace [ str ]({ 'created' : '2022-07-11T14:36:37.166659' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 3 , 'arg:your_name:value' : 'Bob' }, 'models' : [], 'original_execution_time_ms' : 0.0571 , 'output' : 'Hi Bob!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : 'f48e94c7-0815-48b3-a864-41349d3dae84' })]","title":"How to perform prediction with GreatAI"},{"location":"how-to-guides/use-service/#how-to-perform-prediction-with-greatai","text":"After creating a GreatAI service by wrapping your prediction function, and optionally configuring it , it's time to do some prediction. Let's take the following example: greeter.py from great_ai import GreatAI @GreatAI . create def greeter ( your_name : str ) -> str : return f 'Hi { your_name } !'","title":"How to perform prediction with GreatAI"},{"location":"how-to-guides/use-service/#one-off-prediction","text":"Even though greeter is now an instance of GreatAI , you can continue using it as a regular function. >>> greeter ( 'Bob' ) Trace [ str ]({ 'created' : '2022-07-11T14:31:46.183764' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 3 , 'arg:your_name:value' : 'Bob' }, 'models' : [], 'original_execution_time_ms' : 0.0381 , 'output' : 'Hi Bob!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : '7c284fd7-7f0d-4464-b5f8-3ef126df34af' }) As you can see, the original return value is wrapped in a Trace object (which is also persisted in your database of choice). You can access the original value under the output property.","title":"One-off prediction"},{"location":"how-to-guides/use-service/#online-prediction","text":"Likely, the main way you would like to expose your model is through an HTTP API. @GreatAI.create scaffolds many REST API endpoints for your model and creates a FastAPI app available under GreatAI.app . This can be served using uvicorn or any other ASGI server . Since most ML code lives in Jupyter notebooks, therefore, deploying a notebook containing the inference function is supported. To achieve this, uvicorn is wrapped by the great-ai command-line utility which \u2014 among others \u2014 takes care of feeding a notebook into uvicorn . It also supports auto-reloading.","title":"Online prediction"},{"location":"how-to-guides/use-service/#in-development","text":"great-ai greeter.py Success Your model is accessible at localhost:6060 . Some configuration options are also supported. great-ai greeter.py --port 8000 --host 127 .0.0.1 --timeout_keep_alive 10 More options For more options (but no Notebook support), simply use uvicorn for starting your app (available at greeter.app ).","title":"In development"},{"location":"how-to-guides/use-service/#in-production","text":"There are three main approaches for deploying a GreatAI service.","title":"In production"},{"location":"how-to-guides/use-service/#manual-deployment","text":"The app is run in production-mode if the value of the ENVIRONMENT environment variable is set to production . ENVIRONMENT = production great-ai greeter.py Simply run ENVIRONMENT=production great-ai deploy.ipynb in the command-line of a production machine. This is the crudest approach; however, it might be fitting for some contexts.","title":"Manual deployment"},{"location":"how-to-guides/use-service/#containerised-deployment","text":"Run the notebook directly in a container or create a service for it using your favourite container orchestrator. docker run -p 6060 :6060 --volume ` pwd ` :/app --rm \\ schmelczera/great-ai deploy.ipynb You can replace pwd with the path to your code's folder.","title":"Containerised deployment"},{"location":"how-to-guides/use-service/#use-a-platform-as-a-service","text":"Similar to the previous approach, your code will run in a container. However, instead of manually managing it, you can just choose from a plethora of PaaS providers (such as AWS ECS , DO App platform , MLEM , Streamlit ) that take a Docker image as a source and handle the rest of the deployment. To this end, you can also create a custom Docker image. It is especially useful if you have third-party dependencies, such as PyTorch or TensorFlow . FROM schmelczera/great-ai:latest # Remove this block if you don't have a requirements.txt COPY requirements.txt ./ RUN pip install --no-cache-dir --requirement requirements.txt # If you store your models in S3 or GridFS, it may be a # good idea to cache them in the image so that you don't # have to download it each time a container starts RUN large-file --backend s3 --secrets s3.ini --cache my-domain-predictor # Add you application code to the image COPY . . # The default ENTRYPOINT is great-ai, specify its argument using CMD CMD [ \"deploy.ipynb\" ]","title":"Use a Platform-as-a-Service"},{"location":"how-to-guides/use-service/#batch-prediction","text":"Processing larger amounts of data on a single machine is made easy by the GreatAI 's process_batch method. This relies on multiprocessing ( parallel_map ) to take full advantage of all available CPU-cores. >>> greeter . process_batch ([ 'Alice' , 'Bob' ]) [ Trace [ str ]({ 'created' : '2022-07-11T14:36:37.119183' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 5 , 'arg:your_name:value' : 'Alice' }, 'models' : [], 'original_execution_time_ms' : 0.1251 , 'output' : 'Hi Alice!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : '90ffa15f-e839-41c4-8e7a-3211168bc138' }), Trace [ str ]({ 'created' : '2022-07-11T14:36:37.166659' , 'exception' : None , 'feedback' : None , 'logged_values' : { 'arg:your_name:length' : 3 , 'arg:your_name:value' : 'Bob' }, 'models' : [], 'original_execution_time_ms' : 0.0571 , 'output' : 'Hi Bob!' , 'tags' : [ 'greeter' , 'online' , 'development' ], 'trace_id' : 'f48e94c7-0815-48b3-a864-41349d3dae84' })]","title":"Batch prediction"},{"location":"reference/","text":"GreatAI reference # from great_ai import * Core # GreatAI # Bases: Generic [ T , V ] Wrapper for a prediction function providing the implementation of SE4ML best-practices. Provides caching (with argument freezing), a TracingContext during execution, the scaffolding of HTTP endpoints using FastAPI and a dashboard using Dash. Supports wrapping async and synchronous functions while also maintaining correct typing. Attributes: Name Type Description app FastAPI instance wrapping the scaffolded endpoints and the Dash app. version SemVer derived from the app's version and the model names and versions registered through use_model. Source code in great_ai/deploy/great_ai.py 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 class GreatAI ( Generic [ T , V ]): \"\"\"Wrapper for a prediction function providing the implementation of SE4ML best-practices. Provides caching (with argument freezing), a TracingContext during execution, the scaffolding of HTTP endpoints using FastAPI and a dashboard using Dash. Supports wrapping async and synchronous functions while also maintaining correct typing. Attributes: app: FastAPI instance wrapping the scaffolded endpoints and the Dash app. version: SemVer derived from the app's version and the model names and versions registered through use_model. \"\"\" __name__ : str # help for MyPy __doc__ : str # help for MyPy def __init__ ( self , func : Callable [ ... , Union [ V , Awaitable [ V ]]], ): \"\"\"Do not call this function directly, use GreatAI.create instead.\"\"\" func = automatically_decorate_parameters ( func ) get_function_metadata_store ( func ) . is_finalised = True self . _cached_func = self . _get_cached_traced_function ( func ) self . _wrapped_func = wraps ( func )( freeze_arguments ( self . _cached_func )) wraps ( func )( self ) self . __doc__ = ( f \"GreatAI wrapper for interacting with the ` { self . __name__ } ` \" + f \"function. \\n\\n { dedent ( self . __doc__ or '' ) } \" ) self . version = str ( get_context () . version ) flat_model_versions = \".\" . join ( f \" { k } -v { v } \" for k , v in model_versions ) if flat_model_versions : self . version += f \"+ { flat_model_versions } \" self . app = FastAPI ( title = snake_case_to_text ( self . __name__ ), version = self . version , description = self . __doc__ + f \" \\n\\n Find out more in the [dashboard]( { DASHBOARD_PATH } ).\" , docs_url = None , redoc_url = None , ) self . _bootstrap_rest_api () @overload @staticmethod def create ( # type: ignore # Overloaded function signatures 1 and 2 overlap with incompatible return types # https://github.com/python/mypy/issues/12759 func : Callable [ ... , Awaitable [ V ]], ) -> \"GreatAI[Awaitable[Trace[V]], V]\" : ... @overload @staticmethod def create ( func : Callable [ ... , V ], ) -> \"GreatAI[Trace[V], V]\" : ... @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a [@parameter][great_ai.parameter] or [@use_model][great_ai.use_model] decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see [great_ai.configure][]. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , ) def __call__ ( self , * args : Any , ** kwargs : Any ) -> T : return self . _wrapped_func ( * args , ** kwargs ) @overload def process_batch ( self , batch : Sequence [ Tuple ], * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ True ], do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... @overload def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ False ] = ... , do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : bool = False , do_not_persist_traces : bool = False , ) -> List [ Trace [ V ]]: \"\"\"Map the wrapped function over a list of input_values (`batch`). A wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] providing type-safety and a progressbar through tqdm. Args: batch: A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set `unpack_arguments=True`. concurrency: Number of processes to start. Don't set it too much higher than the number of available CPU cores. unpack_arguments: Expect a list of tuples and unpack the tuples before giving them to the wrapped function. do_not_persist_traces: Don't save the traces in the database. Useful for evaluations run part of the CI. \"\"\" wrapped_function = self . _wrapped_func def inner ( value : Any ) -> T : return ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ) async def inner_async ( value : Any ) -> T : return await cast ( Awaitable , ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ), ) return list ( tqdm ( parallel_map ( inner_async if get_function_metadata_store ( self ) . is_asynchronous else inner , batch , concurrency = concurrency , ), total = len ( batch ), ) ) @staticmethod def _get_cached_traced_function ( func : Callable [ ... , Union [ V , Awaitable [ V ]]] ) -> Callable [ ... , T ]: @lru_cache ( maxsize = get_context () . prediction_cache_size ) def func_in_tracing_context_sync ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = func ( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) @alru_cache ( maxsize = get_context () . prediction_cache_size ) async def func_in_tracing_context_async ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = await cast ( Callable [ ... , Awaitable ], func )( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) return cast ( Callable [ ... , T ], ( func_in_tracing_context_async if get_function_metadata_store ( func ) . is_asynchronous else func_in_tracing_context_sync ), ) def _bootstrap_rest_api ( self , ) -> None : route_config = get_context () . route_config if route_config . prediction_endpoint_enabled : bootstrap_prediction_endpoint ( self . app , self . _wrapped_func ) if route_config . docs_endpoints_enabled : bootstrap_docs_endpoints ( self . app ) if route_config . dashboard_enabled : bootstrap_dashboard ( self . app , function_name = self . __name__ , documentation = self . __doc__ , ) if route_config . trace_endpoints_enabled : bootstrap_trace_endpoints ( self . app ) if route_config . feedback_endpoints_enabled : bootstrap_feedback_endpoints ( self . app ) if route_config . meta_endpoints_enabled : bootstrap_meta_endpoints ( self . app , self . _cached_func , ApiMetadata ( name = self . __name__ , version = self . version , documentation = self . __doc__ , configuration = get_context () . to_flat_dict (), ), ) create ( func ) staticmethod # Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a @parameter or @use_model decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the .output property. For configuration options, see great_ai.configure . Examples: >>> @GreatAI . create ... def my_function ( a ): ... return a + 2 >>> my_function ( 3 ) . output 5 >>> @GreatAI . create ... def my_function ( a : int ) -> int : ... return a + 2 >>> my_function ( 3 ) Trace[int]... >>> my_function ( '3' ) . output Traceback (most recent call last): ... TypeError : type of a must be int; got str instead Parameters: Name Type Description Default func Union [ Callable [..., Awaitable [ V ]], Callable [..., V ]] The prediction function that needs to be decorated. required Returns: Type Description Union [ GreatAI[Awaitable[Trace[V]], V] , GreatAI[Trace[V], V] ] A GreatAI instance wrapping func . Source code in great_ai/deploy/great_ai.py 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a [@parameter][great_ai.parameter] or [@use_model][great_ai.use_model] decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see [great_ai.configure][]. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , ) process_batch ( batch , * , concurrency = None , unpack_arguments = False , do_not_persist_traces = False ) # Map the wrapped function over a list of input_values ( batch ). A wrapper over parallel_map providing type-safety and a progressbar through tqdm. Parameters: Name Type Description Default batch Sequence A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set unpack_arguments=True . required concurrency Optional [ int ] Number of processes to start. Don't set it too much higher than the number of available CPU cores. None unpack_arguments bool Expect a list of tuples and unpack the tuples before giving them to the wrapped function. False do_not_persist_traces bool Don't save the traces in the database. Useful for evaluations run part of the CI. False Source code in great_ai/deploy/great_ai.py 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : bool = False , do_not_persist_traces : bool = False , ) -> List [ Trace [ V ]]: \"\"\"Map the wrapped function over a list of input_values (`batch`). A wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] providing type-safety and a progressbar through tqdm. Args: batch: A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set `unpack_arguments=True`. concurrency: Number of processes to start. Don't set it too much higher than the number of available CPU cores. unpack_arguments: Expect a list of tuples and unpack the tuples before giving them to the wrapped function. do_not_persist_traces: Don't save the traces in the database. Useful for evaluations run part of the CI. \"\"\" wrapped_function = self . _wrapped_func def inner ( value : Any ) -> T : return ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ) async def inner_async ( value : Any ) -> T : return await cast ( Awaitable , ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ), ) return list ( tqdm ( parallel_map ( inner_async if get_function_metadata_store ( self ) . is_asynchronous else inner , batch , concurrency = concurrency , ), total = len ( batch ), ) ) configure ( * , version = '0.0.1' , log_level = DEBUG , seed = 42 , tracing_database_factory = None , large_file_implementation = None , should_log_exception_stack = None , prediction_cache_size = 512 , disable_se4ml_banner = False , dashboard_table_size = 50 , route_config = RouteConfig ()) # Set the global configuration used by the great-ai library. You must call configure before calling (or decorating with) any other great-ai function. If tracing_database_factory or large_file_implementation is not specified, their default value is determined based on which TracingDatabase and LargeFile has been configured (e.g.: LargeFileS3.configure_credentials_from_file('s3.ini')), or whether there is any file named s3.ini or mongo.ini in the working directory. Examples: >>> configure ( prediction_cache_size = 0 ) Parameters: Name Type Description Default version Union [ int , str ] The version of your application (using SemVer is recommended). '0.0.1' log_level int Set the default logging level of logging . DEBUG seed int Set seed of random (and numpy if installed) for reproducibility. 42 tracing_database_factory Optional [ Type [ TracingDatabaseDriver ]] Specify a different TracingDatabaseDriver than the one already configured. None large_file_implementation Optional [ Type [ LargeFileBase ]] Specify a different LargeFile than the one already configured. None should_log_exception_stack Optional [ bool ] Log the traces of unhandled exceptions. None prediction_cache_size int Size of the LRU cache applied over the prediction functions. 512 disable_se4ml_banner bool Turn off the warning about the importance of SE4ML best- practices. False dashboard_table_size int Number of rows to display in the dashboard's table. 50 route_config RouteConfig Enable or disable specific HTTP API endpoints. RouteConfig() Source code in great_ai/context.py 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 def configure ( * , version : Union [ int , str ] = \"0.0.1\" , log_level : int = DEBUG , seed : int = 42 , tracing_database_factory : Optional [ Type [ TracingDatabaseDriver ]] = None , large_file_implementation : Optional [ Type [ LargeFileBase ]] = None , should_log_exception_stack : Optional [ bool ] = None , prediction_cache_size : int = 512 , disable_se4ml_banner : bool = False , dashboard_table_size : int = 50 , route_config : RouteConfig = RouteConfig (), ) -> None : \"\"\"Set the global configuration used by the great-ai library. You must call `configure` before calling (or decorating with) any other great-ai function. If `tracing_database_factory` or `large_file_implementation` is not specified, their default value is determined based on which TracingDatabase and LargeFile has been configured (e.g.: LargeFileS3.configure_credentials_from_file('s3.ini')), or whether there is any file named s3.ini or mongo.ini in the working directory. Examples: >>> configure(prediction_cache_size=0) Arguments: version: The version of your application (using SemVer is recommended). log_level: Set the default logging level of `logging`. seed: Set seed of `random` (and `numpy` if installed) for reproducibility. tracing_database_factory: Specify a different TracingDatabaseDriver than the one already configured. large_file_implementation: Specify a different LargeFile than the one already configured. should_log_exception_stack: Log the traces of unhandled exceptions. prediction_cache_size: Size of the LRU cache applied over the prediction functions. disable_se4ml_banner: Turn off the warning about the importance of SE4ML best- practices. dashboard_table_size: Number of rows to display in the dashboard's table. route_config: Enable or disable specific HTTP API endpoints. \"\"\" global _context logger = get_logger ( \"great_ai\" , level = log_level ) if _context is not None : logger . error ( \"Configuration has been already initialised, overwriting. \\n \" + \"Make sure to call `configure()` before importing your application code.\" ) is_production = _is_in_production_mode ( logger = logger ) _set_seed ( seed ) tracing_database_factory = _initialize_tracing_database ( tracing_database_factory , logger = logger ) tracing_database = tracing_database_factory () if not tracing_database . is_production_ready : message = f \"\"\"The selected tracing database ( { tracing_database_factory . __name__ } ) is not recommended for production\"\"\" if is_production : logger . error ( message ) else : logger . warning ( message ) _context = Context ( version = version , tracing_database = tracing_database , large_file_implementation = _initialize_large_file ( large_file_implementation , logger = logger ), is_production = is_production , logger = logger , should_log_exception_stack = not is_production if should_log_exception_stack is None else should_log_exception_stack , prediction_cache_size = prediction_cache_size , dashboard_table_size = dashboard_table_size , route_config = route_config , ) logger . info ( f \"GreatAI (v { __version__ } ): configured \u2705\" ) for k , v in get_context () . to_flat_dict () . items (): logger . info ( f \" { LIST_ITEM_PREFIX }{ k } : { v } \" ) if not is_production and not disable_se4ml_banner : logger . warning ( \"You still need to check whether you follow all best practices before \" \"trusting your deployment.\" ) logger . warning ( f \"> Find out more at { SE4ML_WEBSITE } \" ) save_model ( model , key , * , keep_last_n = None ) # Save (and optionally serialise) a model in order to use by use_model . The model can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case model is an object, it is serialised using dill before uploading it. Examples: >>> from great_ai import use_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Parameters: Name Type Description Default model Union [ Path , str , object ] The object or path to be uploaded. required key str The model's name. required keep_last_n Optional [ int ] If specified, remove old models and only keep the latest n. Directly passed to LargeFile. None Returns: Type Description str The key and version of the saved model separated by a colon. Example: \"key:version\" Source code in great_ai/models/save_model.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 def save_model ( model : Union [ Path , str , object ], key : str , * , keep_last_n : Optional [ int ] = None ) -> str : \"\"\"Save (and optionally serialise) a model in order to use by `use_model`. The `model` can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case `model` is an object, it is serialised using `dill` before uploading it. Examples: >>> from great_ai import use_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: model: The object or path to be uploaded. key: The model's name. keep_last_n: If specified, remove old models and only keep the latest n. Directly passed to LargeFile. Returns: The key and version of the saved model separated by a colon. Example: \"key:version\" \"\"\" file = get_context () . large_file_implementation ( name = key , mode = \"wb\" , keep_last_n = keep_last_n ) if isinstance ( model , Path ) or isinstance ( model , str ): file . push ( model ) else : with file as f : dump ( model , f ) get_context () . logger . info ( f \"Model { key } uploaded with version { file . version } \" ) return f \" { key } : { file . version } \" use_model ( key , * , version = 'latest' , model_kwarg_name = 'model' ) # Inject a model into a function. Load a model specified by key and version using the currently active LargeFile implementation. If it's a single object, it is deserialised using dill . If it's a directory of files, a pathlib.Path instance is given. By default, the function's model parameter is replaced by the loaded model. This can be customised by changing model_kwarg_name . Multiple models can be loaded by decorating the same function with use_model multiple times. Examples: >>> from great_ai import save_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Args: key: The model's name as stored by the LargeFile implementation. version: The model's version as stored by the LargeFile implementation. model_kwarg_name: the parameter to use for injecting the loaded model Returns: A decorator for model injection. Source code in great_ai/models/use_model.py 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 def use_model ( key : str , * , version : Union [ int , Literal [ \"latest\" ]] = \"latest\" , model_kwarg_name : str = \"model\" , ) -> Callable [[ F ], F ]: \"\"\"Inject a model into a function. Load a model specified by `key` and `version` using the currently active `LargeFile` implementation. If it's a single object, it is deserialised using `dill`. If it's a directory of files, a `pathlib.Path` instance is given. By default, the function's `model` parameter is replaced by the loaded model. This can be customised by changing `model_kwarg_name`. Multiple models can be loaded by decorating the same function with `use_model` multiple times. Examples: >>> from great_ai import save_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: key: The model's name as stored by the LargeFile implementation. version: The model's version as stored by the LargeFile implementation. model_kwarg_name: the parameter to use for injecting the loaded model Returns: A decorator for model injection. \"\"\" assert ( isinstance ( version , int ) or version == \"latest\" ), \"Only integers or the string literal `latest` is allowed as a version\" model , actual_version = _load_model ( key = key , version = None if version == \"latest\" else version , ) def decorator ( func : F ) -> F : assert_function_is_not_finalised ( func ) store = get_function_metadata_store ( func ) store . model_parameter_names . append ( model_kwarg_name ) @wraps ( func ) def wrapper ( * args : List [ Any ], ** kwargs : Dict [ str , Any ]) -> Any : tracing_context = TracingContext . get_current_tracing_context () if tracing_context : tracing_context . log_model ( Model ( key = key , version = actual_version )) return func ( * args , ** kwargs , ** { model_kwarg_name : model }) return cast ( F , wrapper ) return decorator parameter ( parameter_name , * , validate = lambda _ : True , disable_logging = False ) # Control the validation and logging of function parameters. Examples: >>> @parameter ( 'a' ) ... def my_function ( a : int ): ... return a + 2 >>> my_function ( 4 ) 6 >>> my_function ( '3' ) Traceback (most recent call last): ... TypeError : type of a must be int; got str instead >>> @parameter ( 'positive_a' , validate = lambda v : v > 0 ) ... def my_function ( positive_a : int ): ... return a + 2 >>> my_function ( - 1 ) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError : ... Parameters: Name Type Description Default parameter_name str Name of parameter to consider required validate Callable [[ Any ], bool ] Optional validate to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. lambda _: True disable_logging bool Do not save the value in any active TracingContext. False Returns: Type Description Callable [[ F ], F ] A decorator for argument validation. Source code in great_ai/parameters/parameter.py 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 def parameter ( parameter_name : str , * , validate : Callable [[ Any ], bool ] = lambda _ : True , disable_logging : bool = False , ) -> Callable [[ F ], F ]: \"\"\"Control the validation and logging of function parameters. Examples: >>> @parameter('a') ... def my_function(a: int): ... return a + 2 >>> my_function(4) 6 >>> my_function('3') Traceback (most recent call last): ... TypeError: type of a must be int; got str instead >>> @parameter('positive_a', validate=lambda v: v > 0) ... def my_function(positive_a: int): ... return a + 2 >>> my_function(-1) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError: ... Args: parameter_name: Name of parameter to consider validate: Optional validate to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. disable_logging: Do not save the value in any active TracingContext. Returns: A decorator for argument validation. \"\"\" def decorator ( func : F ) -> F : get_function_metadata_store ( func ) . input_parameter_names . append ( parameter_name ) assert_function_is_not_finalised ( func ) actual_name = f \"arg: { parameter_name } \" @wraps ( func ) def wrapper ( * args : Any , ** kwargs : Dict [ str , Any ]) -> Any : arguments = get_arguments ( func , args , kwargs ) argument = arguments . get ( parameter_name ) expected_type = func . __annotations__ . get ( parameter_name ) if expected_type is not None : check_type ( parameter_name , argument , expected_type ) if not validate ( argument ): raise ArgumentValidationError ( f \"\"\"Argument { parameter_name } in { func . __name__ } did not pass validation\"\"\" ) context = TracingContext . get_current_tracing_context () if context and not disable_logging : context . log_value ( name = f \" { actual_name } :value\" , value = argument ) if isinstance ( argument , str ): context . log_value ( name = f \" { actual_name } :length\" , value = len ( argument )) return func ( * args , ** kwargs ) return cast ( F , wrapper ) return decorator log_metric ( argument_name , value ) # Source code in great_ai/parameters/log_metric.py 8 9 10 11 12 13 14 15 def log_metric ( argument_name : str , value : Any ) -> None : tracing_context = TracingContext . get_current_tracing_context () caller = inspect . stack ()[ 1 ] . function actual_name = f \"metric: { caller } : { argument_name } \" if tracing_context : tracing_context . log_value ( name = actual_name , value = value ) get_context () . logger . info ( f \" { actual_name } = { value } \" ) Remote calls # call_remote_great_ai ( base_uri , data , retry_count = 4 , timeout_in_seconds = 300 , model_class = None ) # Communicate with a GreatAI object through an HTTP request. Wrapper over call_remote_great_ai_async making it synchronous. For more info, see call_remote_great_ai_async . Source code in great_ai/remote/call_remote_great_ai.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 def call_remote_great_ai ( base_uri : str , data : Mapping [ str , Any ], retry_count : int = 4 , timeout_in_seconds : Optional [ int ] = 300 , model_class : Optional [ Type [ T ]] = None , ) -> Trace [ T ]: \"\"\"Communicate with a GreatAI object through an HTTP request. Wrapper over `call_remote_great_ai_async` making it synchronous. For more info, see `call_remote_great_ai_async`. \"\"\" try : asyncio . get_running_loop () raise Exception ( f \"Already running in an event loop, you have to call ` { call_remote_great_ai_async . __name__ } `\" ) except RuntimeError : pass future = call_remote_great_ai_async ( base_uri = base_uri , data = data , retry_count = retry_count , timeout_in_seconds = timeout_in_seconds , model_class = model_class , ) return asyncio . run ( future ) call_remote_great_ai_async ( base_uri , data , retry_count = 4 , timeout_in_seconds = 300 , model_class = None ) async # Communicate with a GreatAI object through an HTTP request. Send a POST request using httpx to implement a remote call. Error-handling and retries are provided by httpx. The return value is inflated into a Trace. If model_class is specified, the original output is deserialised. Parameters: Name Type Description Default base_uri str Address of the remote instance, example: 'http://localhost:6060' required data Mapping [ str , Any ] The input sent as a json to the remote instance. required retry_count int Retry on any HTTP communication failure. 4 timeout_in_seconds Optional [ int ] Overall permissable max length of the request. None means no timeout. 300 model_class Optional [ Type [ T ]] A subtype of BaseModel to be used for deserialising the .output of the trace. None Source code in great_ai/remote/call_remote_great_ai_async.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 async def call_remote_great_ai_async ( base_uri : str , data : Mapping [ str , Any ], retry_count : int = 4 , timeout_in_seconds : Optional [ int ] = 300 , model_class : Optional [ Type [ T ]] = None , ) -> Trace [ T ]: \"\"\"Communicate with a GreatAI object through an HTTP request. Send a POST request using [httpx](https://www.python-httpx.org/) to implement a remote call. Error-handling and retries are provided by httpx. The return value is inflated into a Trace. If `model_class` is specified, the original output is deserialised. Args: base_uri: Address of the remote instance, example: 'http://localhost:6060' data: The input sent as a json to the remote instance. retry_count: Retry on any HTTP communication failure. timeout_in_seconds: Overall permissable max length of the request. `None` means no timeout. model_class: A subtype of BaseModel to be used for deserialising the `.output` of the trace. \"\"\" if base_uri . endswith ( \"/\" ): base_uri = base_uri [: - 1 ] if not base_uri . endswith ( \"/predict\" ): base_uri = f \" { base_uri } /predict\" transport = httpx . AsyncHTTPTransport ( retries = retry_count ) try : async with httpx . AsyncClient ( transport = transport , timeout = timeout_in_seconds ) as client : response = await client . post ( base_uri , json = data ) try : response . raise_for_status () except Exception : raise RemoteCallError ( f \"Unexpected status code, reason: { response . text } \" ) except Exception as e : raise RemoteCallError from e try : trace = response . json () except Exception : raise RemoteCallError ( f \"JSON parsing failed { response . text } \" , ) try : if model_class is not None : trace [ \"output\" ] = model_class . parse_obj ( trace [ \"output\" ]) return Trace . parse_obj ( trace ) except Exception : raise RemoteCallError ( \"Could not parse Trace\" ) Ground-truth # add_ground_truth ( inputs , expected_outputs , * , tags = [], train_split_ratio = 1 , test_split_ratio = 0 , validation_split_ratio = 0 ) # Add training data (with optional train-test splitting). Add and tag data-points, wrap them into traces. The inputs are available via the .input property, while expected_outputs under both the .output and .feedback properties. All generated traces are tagged with ground_truth by default. Additional tags can be also provided. Using the split_ratio arguments, tags can be given randomly with a user-defined distribution. Only the ratio of the splits matter, they don't have to add up to 1. Examples: >>> add_ground_truth ( ... [ 1 , 2 , 3 ], ... [ 'odd' , 'even' , 'odd' ], ... tags = 'my_tag' , ... train_split_ratio = 1 , ... test_split_ratio = 1 , ... validation_split_ratio = 0.5 , ... ) >>> add_ground_truth ( ... [ 1 , 2 ], ... [ 'odd' , 'even' , 'odd' ], ... tags = 'my_tag' , ... train_split_ratio = 1 , ... test_split_ratio = 1 , ... validation_split_ratio = 0.5 , ... ) Traceback (most recent call last): ... AssertionError : The length of the inputs and expected_outputs must be equal Parameters: Name Type Description Default inputs Iterable [ Any ] The inputs. (X in scikit-learn) required expected_outputs Iterable [ T ] The ground-truth values corresponding to the inputs. (y in scikit-learn) required tags Union [ List [ str ], str ] A single tag or a list of tags to append to each generated trace's tags. [] train_split_ratio float The probability-weight of giving each trace the train tag. 1 test_split_ratio float The probability-weight of giving each trace the test tag. 0 validation_split_ratio float The probability-weight of giving each trace the validation tag. 0 Source code in great_ai/tracing/add_ground_truth.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 def add_ground_truth ( inputs : Iterable [ Any ], expected_outputs : Iterable [ T ], * , tags : Union [ List [ str ], str ] = [], train_split_ratio : float = 1 , test_split_ratio : float = 0 , validation_split_ratio : float = 0 ) -> None : \"\"\"Add training data (with optional train-test splitting). Add and tag data-points, wrap them into traces. The `inputs` are available via the `.input` property, while `expected_outputs` under both the `.output` and `.feedback` properties. All generated traces are tagged with `ground_truth` by default. Additional tags can be also provided. Using the `split_ratio` arguments, tags can be given randomly with a user-defined distribution. Only the ratio of the splits matter, they don't have to add up to 1. Examples: >>> add_ground_truth( ... [1, 2, 3], ... ['odd', 'even', 'odd'], ... tags='my_tag', ... train_split_ratio=1, ... test_split_ratio=1, ... validation_split_ratio=0.5, ... ) >>> add_ground_truth( ... [1, 2], ... ['odd', 'even', 'odd'], ... tags='my_tag', ... train_split_ratio=1, ... test_split_ratio=1, ... validation_split_ratio=0.5, ... ) Traceback (most recent call last): ... AssertionError: The length of the inputs and expected_outputs must be equal Args: inputs: The inputs. (X in scikit-learn) expected_outputs: The ground-truth values corresponding to the inputs. (y in scikit-learn) tags: A single tag or a list of tags to append to each generated trace's tags. train_split_ratio: The probability-weight of giving each trace the `train` tag. test_split_ratio: The probability-weight of giving each trace the `test` tag. validation_split_ratio: The probability-weight of giving each trace the `validation` tag. \"\"\" inputs = list ( inputs ) expected_outputs = list ( expected_outputs ) assert len ( inputs ) == len ( expected_outputs ), \"The length of the inputs and expected_outputs must be equal\" tags = tags if isinstance ( tags , list ) else [ tags ] sum_ratio = train_split_ratio + test_split_ratio + validation_split_ratio assert sum_ratio > 0 , \"The sum of the split ratios must be a positive number\" train_split_ratio /= sum_ratio test_split_ratio /= sum_ratio validation_split_ratio /= sum_ratio values = list ( zip ( inputs , expected_outputs )) shuffle ( values ) split_tags = ( [ TRAIN_SPLIT_TAG_NAME ] * ceil ( train_split_ratio * len ( inputs )) + [ TEST_SPLIT_TAG_NAME ] * ceil ( test_split_ratio * len ( inputs )) + [ VALIDATION_SPLIT_TAG_NAME ] * ceil ( validation_split_ratio * len ( inputs )) ) shuffle ( split_tags ) created = datetime . utcnow () . isoformat () traces = [ cast ( Trace [ T ], Trace ( # avoid ValueError: \"Trace\" object has no field \"__orig_class__\" trace_id = str ( uuid4 ()), created = created , original_execution_time_ms = 0 , logged_values = X if isinstance ( X , dict ) else { \"input\" : X }, models = [], output = y , feedback = y , exception = None , tags = [ GROUND_TRUTH_TAG_NAME , split_tag , * tags ], ), ) for (( X , y ), split_tag ) in zip ( values , split_tags ) ] get_context () . tracing_database . save_batch ( traces ) query_ground_truth ( conjunctive_tags = [], * , since = None , until = None , return_max_count = None ) # Return training samples. Combines, filters, and returns data-points that have been either added by add_ground_truth or were the result of a prediction after which their trace got a feedback through the RESP API-s /traces/{trace_id}/feedback endpoint (end-to-end feedback). Filtering can be used to only return points matching all given tags (or the single given tag) and by time of creation. Examples: >>> query_ground_truth () [...] Parameters: Name Type Description Default conjunctive_tags Union [ List [ str ], str ] Single tag or a list of tags which the returned traces have to match. The relationship between the tags is conjunctive (AND). [] since Optional [ datetime ] Only return traces created after the given timestamp. None means no filtering. None until Optional [ datetime ] Only return traces created before the given timestamp. None means no filtering. None return_max_count Optional [ int ] Return at-most this many traces. (take, limit) None Source code in great_ai/tracing/query_ground_truth.py 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 def query_ground_truth ( conjunctive_tags : Union [ List [ str ], str ] = [], * , since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , return_max_count : Optional [ int ] = None ) -> List [ Trace ]: \"\"\"Return training samples. Combines, filters, and returns data-points that have been either added by `add_ground_truth` or were the result of a prediction after which their trace got a feedback through the RESP API-s `/traces/{trace_id}/feedback` endpoint (end-to-end feedback). Filtering can be used to only return points matching all given tags (or the single given tag) and by time of creation. Examples: >>> query_ground_truth() [...] Args: conjunctive_tags: Single tag or a list of tags which the returned traces have to match. The relationship between the tags is conjunctive (AND). since: Only return traces created after the given timestamp. `None` means no filtering. until: Only return traces created before the given timestamp. `None` means no filtering. return_max_count: Return at-most this many traces. (take, limit) \"\"\" tags = ( conjunctive_tags if isinstance ( conjunctive_tags , list ) else [ conjunctive_tags ] ) db = get_context () . tracing_database items , length = db . query ( conjunctive_tags = tags , since = since , until = until , take = return_max_count , has_feedback = True , ) return items delete_ground_truth ( conjunctive_tags = [], * , since = None , until = None ) # Delete traces matching the given criteria. Takes the same arguments as query_ground_truth but instead of returning them, it simply deletes them. You can rely on the efficiency of the delete's implementation. Examples: >>> delete_ground_truth ([ 'train' , 'test' , 'validation' ]) Parameters: Name Type Description Default conjunctive_tags Union [ List [ str ], str ] Single tag or a list of tags which the deleted traces have to match. The relationship between the tags is conjunctive (AND). [] since Optional [ datetime ] Only delete traces created after the given timestamp. None means no filtering. None until Optional [ datetime ] Only delete traces created before the given timestamp. None means no filtering. None Source code in great_ai/tracing/delete_ground_truth.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 def delete_ground_truth ( conjunctive_tags : Union [ List [ str ], str ] = [], * , since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , ) -> None : \"\"\"Delete traces matching the given criteria. Takes the same arguments as `query_ground_truth` but instead of returning them, it simply deletes them. You can rely on the efficiency of the delete's implementation. Examples: >>> delete_ground_truth(['train', 'test', 'validation']) Args: conjunctive_tags: Single tag or a list of tags which the deleted traces have to match. The relationship between the tags is conjunctive (AND). since: Only delete traces created after the given timestamp. `None` means no filtering. until: Only delete traces created before the given timestamp. `None` means no filtering. \"\"\" tags = ( conjunctive_tags if isinstance ( conjunctive_tags , list ) else [ conjunctive_tags ] ) db = get_context () . tracing_database items , length = db . query ( conjunctive_tags = tags , until = until , since = since , has_feedback = True ) db . delete_batch ([ i . trace_id for i in items ]) Tracing databases # TracingDatabaseDriver # Bases: ABC Interface expected from a database to be used for storing traces. Source code in great_ai/persistence/tracing_database_driver.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 class TracingDatabaseDriver ( ABC ): \"\"\"Interface expected from a database to be used for storing traces.\"\"\" is_production_ready : bool initialized : bool = False @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()}) @classmethod def configure_credentials ( cls , ) -> None : cls . initialized = True @abstractmethod def save ( self , document : Trace ) -> str : pass @abstractmethod def save_batch ( self , documents : List [ Trace ], ) -> List [ str ]: pass @abstractmethod def get ( self , id : str ) -> Optional [ Trace ]: pass @abstractmethod def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], until : Optional [ datetime ] = None , since : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [] ) -> Tuple [ List [ Trace ], int ]: pass @abstractmethod def update ( self , id : str , new_version : Trace ) -> None : pass @abstractmethod def delete ( self , id : str ) -> None : pass @abstractmethod def delete_batch ( self , ids : List [ str ], ) -> None : pass MongoDbDriver # Bases: TracingDatabaseDriver TracingDatabaseDriver implementation using MongoDB as a backend. A production-ready database driver suitable for efficiently handling semi-structured data. Checkout MongoDB Atlas for a hosted MongoDB solution. Source code in great_ai/persistence/mongodb_driver.py 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 class MongoDbDriver ( TracingDatabaseDriver ): \"\"\"TracingDatabaseDriver implementation using MongoDB as a backend. A production-ready database driver suitable for efficiently handling semi-structured data. Checkout [MongoDB Atlas](https://www.mongodb.com/cloud/atlas/register) for a hosted MongoDB solution. \"\"\" is_production_ready = True mongo_connection_string : str mongo_database : str def __init__ ( self ) -> None : super () . __init__ () if self . mongo_connection_string is None or self . mongo_database is None : raise ValueError ( \"Please configure the MongoDB access options by calling \" \"MongoDbDriver.configure_credentials\" ) with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . create_index ( [( \"tags\" , ASCENDING ), ( \"created\" , DESCENDING )], background = True ) @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : \"\"\"Configure the connection details for MongoDB. Args: mongo_connection_string: For example: 'mongodb://my_user:my_pass@localhost:27017' mongo_database: Name of the database to use. If doesn't exist, it is created and initialised. \"\"\" cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () def save ( self , trace : Trace ) -> str : serialized = trace . to_flat_dict () serialized [ \"_id\" ] = trace . trace_id with MongoClient [ Any ]( self . mongo_connection_string ) as client : return client [ self . mongo_database ] . traces . insert_one ( serialized ) . inserted_id def save_batch ( self , documents : List [ Trace ]) -> List [ str ]: serialized = [ d . to_flat_dict () for d in documents ] for s in serialized : s [ \"_id\" ] = s [ \"trace_id\" ] with MongoClient [ Any ]( self . mongo_connection_string ) as client : return ( client [ self . mongo_database ] . traces . insert_many ( serialized , ordered = False ) . inserted_ids ) def get ( self , id : str ) -> Optional [ Trace ]: with MongoClient [ Any ]( self . mongo_connection_string ) as client : value = client [ self . mongo_database ] . traces . find_one ( id ) if value : value = Trace . parse_obj ( value ) return value def _get_operator ( self , filter : Filter ) -> str : if filter . operator == \"contains\" and not isinstance ( filter . value , str ): return operator_mapping [ \"=\" ] return operator_mapping [ filter . operator ] def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [], ) -> Tuple [ List [ Trace ], int ]: query : Dict [ str , Any ] = { \"filter\" : {}, } and_query : List [ Dict [ str , Any ]] = [] and_query . extend ({ \"tags\" : tag } for tag in conjunctive_tags ) and_query . extend ( { f . property : { self . _get_operator ( f ): f . value }} for f in conjunctive_filters ) if not and_query : and_query . append ({}) if since : and_query . append ({ \"created\" : { \"$gte\" : since }}) if until : and_query . append ({ \"created\" : { \"$lte\" : until }}) if has_feedback is not None : and_query . append ( { \"feedback\" : { \"$ne\" : None }} if has_feedback else { \"feedback\" : None } ) query [ \"filter\" ][ \"$and\" ] = and_query with MongoClient [ Any ]( self . mongo_connection_string ) as client : count = client [ self . mongo_database ] . traces . count_documents ( ** query ) if skip : query [ \"skip\" ] = skip if take : query [ \"limit\" ] = take query [ \"sort\" ] = [ ( col . column_id , 1 if col . direction == \"asc\" else - 1 ) for col in sort_by ] with client [ self . mongo_database ] . traces . find ( ** query ) as cursor : documents = [ Trace [ Any ] . parse_obj ( t ) for t in cursor ] return documents , count def update ( self , id : str , new_version : Trace ) -> None : serialized = new_version . to_flat_dict () serialized [ \"_id\" ] = new_version . trace_id with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . update_one ({ \"_id\" : id }, serialized ) def delete ( self , id : str ) -> None : with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . delete_one ({ \"_id\" : id }) def delete_batch ( self , ids : List [ str ]) -> None : with MongoClient [ Any ]( self . mongo_connection_string ) as client : for c in chunk ( ids , chunk_size = 10000 ): # avoid: 'delete' command document too large delete_filter = { \"_id\" : { \"$in\" : c }} client [ self . mongo_database ] . traces . delete_many ( delete_filter ) configure_credentials ( * , mongo_connection_string , mongo_database , ** _ ) classmethod # Configure the connection details for MongoDB. Parameters: Name Type Description Default mongo_connection_string str For example: 'mongodb://my_user:my_pass@localhost:27017' required mongo_database str Name of the database to use. If doesn't exist, it is created and initialised. required Source code in great_ai/persistence/mongodb_driver.py 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : \"\"\"Configure the connection details for MongoDB. Args: mongo_connection_string: For example: 'mongodb://my_user:my_pass@localhost:27017' mongo_database: Name of the database to use. If doesn't exist, it is created and initialised. \"\"\" cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () ParallelTinyDbDriver # Bases: TracingDatabaseDriver TracingDatabaseDriver with TinyDB as a backend. Saves the database as a JSON into a single file. Highly inefficient on inserting, not advised for production use. A multiprocessing lock protects the database file to avoid parallelisation issues. Source code in great_ai/persistence/parallel_tinydb_driver.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 class ParallelTinyDbDriver ( TracingDatabaseDriver ): \"\"\"TracingDatabaseDriver with TinyDB as a backend. Saves the database as a JSON into a single file. Highly inefficient on inserting, not advised for production use. A multiprocessing lock protects the database file to avoid parallelisation issues. \"\"\" is_production_ready = False path_to_db = Path ( DEFAULT_TRACING_DB_FILENAME ) def save ( self , trace : Trace ) -> str : return self . _safe_execute ( lambda db : db . insert ( trace . dict ())) def save_batch ( self , documents : List [ Trace ]) -> List [ str ]: traces = [ d . dict () for d in documents ] return self . _safe_execute ( lambda db : db . insert_multiple ( traces )) def get ( self , id : str ) -> Optional [ Trace ]: value = self . _safe_execute ( lambda db : db . get ( lambda d : d [ \"trace_id\" ] == id )) if value : value = Trace . parse_obj ( value ) return value def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [] ) -> Tuple [ List [ Trace ], int ]: def does_match ( d : Dict [ str , Any ]) -> bool : return ( not set ( conjunctive_tags ) - set ( d [ \"tags\" ]) and ( since is None or datetime . fromisoformat ( d [ \"created\" ]) >= since ) and ( until is None or datetime . fromisoformat ( d [ \"created\" ]) <= until ) and ( has_feedback is None or has_feedback == ( d [ \"feedback\" ] is not None ) ) ) documents : List [ Trace ] = [ Trace . parse_obj ( t ) for t in self . _safe_execute ( lambda db : db . search ( does_match )) ] if not documents : return [], 0 df = pd . DataFrame ([ d . to_flat_dict () for d in documents ]) for f in conjunctive_filters : operator = f . operator . lower () if operator in operator_mapping : df = df . loc [ getattr ( df [ f . property ], operator_mapping [ f . operator ])( f . value ) ] elif operator == \"contains\" : df = df . loc [ df [ f . property ] . str . contains ( f . value , case = False )] if sort_by : df . sort_values ( [ col . column_id for col in sort_by ], ascending = [ col . direction == \"asc\" for col in sort_by ], inplace = True , ) count = len ( df ) result = df . iloc [ skip :] if take is None else df . iloc [ skip : skip + take ] return [ next ( d for d in documents if d . trace_id == trace_id ) for trace_id in result [ \"trace_id\" ] ], count def update ( self , id : str , new_version : Trace ) -> None : self . _safe_execute ( lambda db : db . update ( new_version . dict (), lambda d : d [ \"trace_id\" ] == id ) ) def delete ( self , id : str ) -> None : self . _safe_execute ( lambda db : db . remove ( lambda d : d [ \"trace_id\" ] == id )) def delete_batch ( self , ids : List [ str ]) -> None : for i in ids : self . delete ( i ) def _safe_execute ( self , func : Callable [[ TinyDB ], Any ]) -> Any : with lock : with TinyDB ( self . path_to_db ) as db : return func ( db )","title":"GreatAI reference"},{"location":"reference/#greatai-reference","text":"from great_ai import *","title":"GreatAI reference"},{"location":"reference/#core","text":"","title":"Core"},{"location":"reference/#great_ai.GreatAI","text":"Bases: Generic [ T , V ] Wrapper for a prediction function providing the implementation of SE4ML best-practices. Provides caching (with argument freezing), a TracingContext during execution, the scaffolding of HTTP endpoints using FastAPI and a dashboard using Dash. Supports wrapping async and synchronous functions while also maintaining correct typing. Attributes: Name Type Description app FastAPI instance wrapping the scaffolded endpoints and the Dash app. version SemVer derived from the app's version and the model names and versions registered through use_model. Source code in great_ai/deploy/great_ai.py 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 class GreatAI ( Generic [ T , V ]): \"\"\"Wrapper for a prediction function providing the implementation of SE4ML best-practices. Provides caching (with argument freezing), a TracingContext during execution, the scaffolding of HTTP endpoints using FastAPI and a dashboard using Dash. Supports wrapping async and synchronous functions while also maintaining correct typing. Attributes: app: FastAPI instance wrapping the scaffolded endpoints and the Dash app. version: SemVer derived from the app's version and the model names and versions registered through use_model. \"\"\" __name__ : str # help for MyPy __doc__ : str # help for MyPy def __init__ ( self , func : Callable [ ... , Union [ V , Awaitable [ V ]]], ): \"\"\"Do not call this function directly, use GreatAI.create instead.\"\"\" func = automatically_decorate_parameters ( func ) get_function_metadata_store ( func ) . is_finalised = True self . _cached_func = self . _get_cached_traced_function ( func ) self . _wrapped_func = wraps ( func )( freeze_arguments ( self . _cached_func )) wraps ( func )( self ) self . __doc__ = ( f \"GreatAI wrapper for interacting with the ` { self . __name__ } ` \" + f \"function. \\n\\n { dedent ( self . __doc__ or '' ) } \" ) self . version = str ( get_context () . version ) flat_model_versions = \".\" . join ( f \" { k } -v { v } \" for k , v in model_versions ) if flat_model_versions : self . version += f \"+ { flat_model_versions } \" self . app = FastAPI ( title = snake_case_to_text ( self . __name__ ), version = self . version , description = self . __doc__ + f \" \\n\\n Find out more in the [dashboard]( { DASHBOARD_PATH } ).\" , docs_url = None , redoc_url = None , ) self . _bootstrap_rest_api () @overload @staticmethod def create ( # type: ignore # Overloaded function signatures 1 and 2 overlap with incompatible return types # https://github.com/python/mypy/issues/12759 func : Callable [ ... , Awaitable [ V ]], ) -> \"GreatAI[Awaitable[Trace[V]], V]\" : ... @overload @staticmethod def create ( func : Callable [ ... , V ], ) -> \"GreatAI[Trace[V], V]\" : ... @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a [@parameter][great_ai.parameter] or [@use_model][great_ai.use_model] decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see [great_ai.configure][]. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , ) def __call__ ( self , * args : Any , ** kwargs : Any ) -> T : return self . _wrapped_func ( * args , ** kwargs ) @overload def process_batch ( self , batch : Sequence [ Tuple ], * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ True ], do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... @overload def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : Literal [ False ] = ... , do_not_persist_traces : bool = ... , ) -> List [ Trace [ V ]]: ... def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : bool = False , do_not_persist_traces : bool = False , ) -> List [ Trace [ V ]]: \"\"\"Map the wrapped function over a list of input_values (`batch`). A wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] providing type-safety and a progressbar through tqdm. Args: batch: A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set `unpack_arguments=True`. concurrency: Number of processes to start. Don't set it too much higher than the number of available CPU cores. unpack_arguments: Expect a list of tuples and unpack the tuples before giving them to the wrapped function. do_not_persist_traces: Don't save the traces in the database. Useful for evaluations run part of the CI. \"\"\" wrapped_function = self . _wrapped_func def inner ( value : Any ) -> T : return ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ) async def inner_async ( value : Any ) -> T : return await cast ( Awaitable , ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ), ) return list ( tqdm ( parallel_map ( inner_async if get_function_metadata_store ( self ) . is_asynchronous else inner , batch , concurrency = concurrency , ), total = len ( batch ), ) ) @staticmethod def _get_cached_traced_function ( func : Callable [ ... , Union [ V , Awaitable [ V ]]] ) -> Callable [ ... , T ]: @lru_cache ( maxsize = get_context () . prediction_cache_size ) def func_in_tracing_context_sync ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = func ( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) @alru_cache ( maxsize = get_context () . prediction_cache_size ) async def func_in_tracing_context_async ( * args : Any , do_not_persist_traces : bool = False , ** kwargs : Any , ) -> T : with TracingContext [ V ]( func . __name__ , do_not_persist_traces = do_not_persist_traces ) as t : result = await cast ( Callable [ ... , Awaitable ], func )( * args , ** kwargs ) return cast ( T , t . finalise ( output = result )) return cast ( Callable [ ... , T ], ( func_in_tracing_context_async if get_function_metadata_store ( func ) . is_asynchronous else func_in_tracing_context_sync ), ) def _bootstrap_rest_api ( self , ) -> None : route_config = get_context () . route_config if route_config . prediction_endpoint_enabled : bootstrap_prediction_endpoint ( self . app , self . _wrapped_func ) if route_config . docs_endpoints_enabled : bootstrap_docs_endpoints ( self . app ) if route_config . dashboard_enabled : bootstrap_dashboard ( self . app , function_name = self . __name__ , documentation = self . __doc__ , ) if route_config . trace_endpoints_enabled : bootstrap_trace_endpoints ( self . app ) if route_config . feedback_endpoints_enabled : bootstrap_feedback_endpoints ( self . app ) if route_config . meta_endpoints_enabled : bootstrap_meta_endpoints ( self . app , self . _cached_func , ApiMetadata ( name = self . __name__ , version = self . version , documentation = self . __doc__ , configuration = get_context () . to_flat_dict (), ), )","title":"GreatAI"},{"location":"reference/#great_ai.deploy.great_ai.GreatAI.create","text":"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a @parameter or @use_model decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the .output property. For configuration options, see great_ai.configure . Examples: >>> @GreatAI . create ... def my_function ( a ): ... return a + 2 >>> my_function ( 3 ) . output 5 >>> @GreatAI . create ... def my_function ( a : int ) -> int : ... return a + 2 >>> my_function ( 3 ) Trace[int]... >>> my_function ( '3' ) . output Traceback (most recent call last): ... TypeError : type of a must be int; got str instead Parameters: Name Type Description Default func Union [ Callable [..., Awaitable [ V ]], Callable [..., V ]] The prediction function that needs to be decorated. required Returns: Type Description Union [ GreatAI[Awaitable[Trace[V]], V] , GreatAI[Trace[V], V] ] A GreatAI instance wrapping func . Source code in great_ai/deploy/great_ai.py 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 @staticmethod def create ( func : Union [ Callable [ ... , Awaitable [ V ]], Callable [ ... , V ]], ) -> Union [ \"GreatAI[Awaitable[Trace[V]], V]\" , \"GreatAI[Trace[V], V]\" ]: \"\"\"Decorate a function by wrapping it in a GreatAI instance. The function can be typed, synchronous or async. If it has unwrapped parameters (parameters not affected by a [@parameter][great_ai.parameter] or [@use_model][great_ai.use_model] decorator), those will be automatically wrapped. The return value is replaced by a Trace (or Awaitable[Trace]), while the original return value is available under the `.output` property. For configuration options, see [great_ai.configure][]. Examples: >>> @GreatAI.create ... def my_function(a): ... return a + 2 >>> my_function(3).output 5 >>> @GreatAI.create ... def my_function(a: int) -> int: ... return a + 2 >>> my_function(3) Trace[int]... >>> my_function('3').output Traceback (most recent call last): ... TypeError: type of a must be int; got str instead Args: func: The prediction function that needs to be decorated. Returns: A GreatAI instance wrapping `func`. \"\"\" return GreatAI [ Trace [ V ], V ]( func , )","title":"create()"},{"location":"reference/#great_ai.deploy.great_ai.GreatAI.process_batch","text":"Map the wrapped function over a list of input_values ( batch ). A wrapper over parallel_map providing type-safety and a progressbar through tqdm. Parameters: Name Type Description Default batch Sequence A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set unpack_arguments=True . required concurrency Optional [ int ] Number of processes to start. Don't set it too much higher than the number of available CPU cores. None unpack_arguments bool Expect a list of tuples and unpack the tuples before giving them to the wrapped function. False do_not_persist_traces bool Don't save the traces in the database. Useful for evaluations run part of the CI. False Source code in great_ai/deploy/great_ai.py 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 def process_batch ( self , batch : Sequence , * , concurrency : Optional [ int ] = None , unpack_arguments : bool = False , do_not_persist_traces : bool = False , ) -> List [ Trace [ V ]]: \"\"\"Map the wrapped function over a list of input_values (`batch`). A wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] providing type-safety and a progressbar through tqdm. Args: batch: A list of arguments for the original (wrapped) function. If the function expects multiple arguments, provide a list of tuples and set `unpack_arguments=True`. concurrency: Number of processes to start. Don't set it too much higher than the number of available CPU cores. unpack_arguments: Expect a list of tuples and unpack the tuples before giving them to the wrapped function. do_not_persist_traces: Don't save the traces in the database. Useful for evaluations run part of the CI. \"\"\" wrapped_function = self . _wrapped_func def inner ( value : Any ) -> T : return ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ) async def inner_async ( value : Any ) -> T : return await cast ( Awaitable , ( wrapped_function ( * value , do_not_persist_traces = do_not_persist_traces ) if unpack_arguments else wrapped_function ( value , do_not_persist_traces = do_not_persist_traces ) ), ) return list ( tqdm ( parallel_map ( inner_async if get_function_metadata_store ( self ) . is_asynchronous else inner , batch , concurrency = concurrency , ), total = len ( batch ), ) )","title":"process_batch()"},{"location":"reference/#great_ai.configure","text":"Set the global configuration used by the great-ai library. You must call configure before calling (or decorating with) any other great-ai function. If tracing_database_factory or large_file_implementation is not specified, their default value is determined based on which TracingDatabase and LargeFile has been configured (e.g.: LargeFileS3.configure_credentials_from_file('s3.ini')), or whether there is any file named s3.ini or mongo.ini in the working directory. Examples: >>> configure ( prediction_cache_size = 0 ) Parameters: Name Type Description Default version Union [ int , str ] The version of your application (using SemVer is recommended). '0.0.1' log_level int Set the default logging level of logging . DEBUG seed int Set seed of random (and numpy if installed) for reproducibility. 42 tracing_database_factory Optional [ Type [ TracingDatabaseDriver ]] Specify a different TracingDatabaseDriver than the one already configured. None large_file_implementation Optional [ Type [ LargeFileBase ]] Specify a different LargeFile than the one already configured. None should_log_exception_stack Optional [ bool ] Log the traces of unhandled exceptions. None prediction_cache_size int Size of the LRU cache applied over the prediction functions. 512 disable_se4ml_banner bool Turn off the warning about the importance of SE4ML best- practices. False dashboard_table_size int Number of rows to display in the dashboard's table. 50 route_config RouteConfig Enable or disable specific HTTP API endpoints. RouteConfig() Source code in great_ai/context.py 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 def configure ( * , version : Union [ int , str ] = \"0.0.1\" , log_level : int = DEBUG , seed : int = 42 , tracing_database_factory : Optional [ Type [ TracingDatabaseDriver ]] = None , large_file_implementation : Optional [ Type [ LargeFileBase ]] = None , should_log_exception_stack : Optional [ bool ] = None , prediction_cache_size : int = 512 , disable_se4ml_banner : bool = False , dashboard_table_size : int = 50 , route_config : RouteConfig = RouteConfig (), ) -> None : \"\"\"Set the global configuration used by the great-ai library. You must call `configure` before calling (or decorating with) any other great-ai function. If `tracing_database_factory` or `large_file_implementation` is not specified, their default value is determined based on which TracingDatabase and LargeFile has been configured (e.g.: LargeFileS3.configure_credentials_from_file('s3.ini')), or whether there is any file named s3.ini or mongo.ini in the working directory. Examples: >>> configure(prediction_cache_size=0) Arguments: version: The version of your application (using SemVer is recommended). log_level: Set the default logging level of `logging`. seed: Set seed of `random` (and `numpy` if installed) for reproducibility. tracing_database_factory: Specify a different TracingDatabaseDriver than the one already configured. large_file_implementation: Specify a different LargeFile than the one already configured. should_log_exception_stack: Log the traces of unhandled exceptions. prediction_cache_size: Size of the LRU cache applied over the prediction functions. disable_se4ml_banner: Turn off the warning about the importance of SE4ML best- practices. dashboard_table_size: Number of rows to display in the dashboard's table. route_config: Enable or disable specific HTTP API endpoints. \"\"\" global _context logger = get_logger ( \"great_ai\" , level = log_level ) if _context is not None : logger . error ( \"Configuration has been already initialised, overwriting. \\n \" + \"Make sure to call `configure()` before importing your application code.\" ) is_production = _is_in_production_mode ( logger = logger ) _set_seed ( seed ) tracing_database_factory = _initialize_tracing_database ( tracing_database_factory , logger = logger ) tracing_database = tracing_database_factory () if not tracing_database . is_production_ready : message = f \"\"\"The selected tracing database ( { tracing_database_factory . __name__ } ) is not recommended for production\"\"\" if is_production : logger . error ( message ) else : logger . warning ( message ) _context = Context ( version = version , tracing_database = tracing_database , large_file_implementation = _initialize_large_file ( large_file_implementation , logger = logger ), is_production = is_production , logger = logger , should_log_exception_stack = not is_production if should_log_exception_stack is None else should_log_exception_stack , prediction_cache_size = prediction_cache_size , dashboard_table_size = dashboard_table_size , route_config = route_config , ) logger . info ( f \"GreatAI (v { __version__ } ): configured \u2705\" ) for k , v in get_context () . to_flat_dict () . items (): logger . info ( f \" { LIST_ITEM_PREFIX }{ k } : { v } \" ) if not is_production and not disable_se4ml_banner : logger . warning ( \"You still need to check whether you follow all best practices before \" \"trusting your deployment.\" ) logger . warning ( f \"> Find out more at { SE4ML_WEBSITE } \" )","title":"configure()"},{"location":"reference/#great_ai.save_model","text":"Save (and optionally serialise) a model in order to use by use_model . The model can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case model is an object, it is serialised using dill before uploading it. Examples: >>> from great_ai import use_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Parameters: Name Type Description Default model Union [ Path , str , object ] The object or path to be uploaded. required key str The model's name. required keep_last_n Optional [ int ] If specified, remove old models and only keep the latest n. Directly passed to LargeFile. None Returns: Type Description str The key and version of the saved model separated by a colon. Example: \"key:version\" Source code in great_ai/models/save_model.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 def save_model ( model : Union [ Path , str , object ], key : str , * , keep_last_n : Optional [ int ] = None ) -> str : \"\"\"Save (and optionally serialise) a model in order to use by `use_model`. The `model` can be a Path or string representing a path in which case the local file/folder is read and saved using the current LargeFile implementation. In case `model` is an object, it is serialised using `dill` before uploading it. Examples: >>> from great_ai import use_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: model: The object or path to be uploaded. key: The model's name. keep_last_n: If specified, remove old models and only keep the latest n. Directly passed to LargeFile. Returns: The key and version of the saved model separated by a colon. Example: \"key:version\" \"\"\" file = get_context () . large_file_implementation ( name = key , mode = \"wb\" , keep_last_n = keep_last_n ) if isinstance ( model , Path ) or isinstance ( model , str ): file . push ( model ) else : with file as f : dump ( model , f ) get_context () . logger . info ( f \"Model { key } uploaded with version { file . version } \" ) return f \" { key } : { file . version } \"","title":"save_model()"},{"location":"reference/#great_ai.use_model","text":"Inject a model into a function. Load a model specified by key and version using the currently active LargeFile implementation. If it's a single object, it is deserialised using dill . If it's a directory of files, a pathlib.Path instance is given. By default, the function's model parameter is replaced by the loaded model. This can be customised by changing model_kwarg_name . Multiple models can be loaded by decorating the same function with use_model multiple times. Examples: >>> from great_ai import save_model >>> save_model ( 3 , 'my_number' ) 'my_number:...' >>> @use_model ( 'my_number' ) ... def my_function ( a , model ): ... return a + model >>> my_function ( 4 ) 7 Args: key: The model's name as stored by the LargeFile implementation. version: The model's version as stored by the LargeFile implementation. model_kwarg_name: the parameter to use for injecting the loaded model Returns: A decorator for model injection. Source code in great_ai/models/use_model.py 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 def use_model ( key : str , * , version : Union [ int , Literal [ \"latest\" ]] = \"latest\" , model_kwarg_name : str = \"model\" , ) -> Callable [[ F ], F ]: \"\"\"Inject a model into a function. Load a model specified by `key` and `version` using the currently active `LargeFile` implementation. If it's a single object, it is deserialised using `dill`. If it's a directory of files, a `pathlib.Path` instance is given. By default, the function's `model` parameter is replaced by the loaded model. This can be customised by changing `model_kwarg_name`. Multiple models can be loaded by decorating the same function with `use_model` multiple times. Examples: >>> from great_ai import save_model >>> save_model(3, 'my_number') 'my_number:...' >>> @use_model('my_number') ... def my_function(a, model): ... return a + model >>> my_function(4) 7 Args: key: The model's name as stored by the LargeFile implementation. version: The model's version as stored by the LargeFile implementation. model_kwarg_name: the parameter to use for injecting the loaded model Returns: A decorator for model injection. \"\"\" assert ( isinstance ( version , int ) or version == \"latest\" ), \"Only integers or the string literal `latest` is allowed as a version\" model , actual_version = _load_model ( key = key , version = None if version == \"latest\" else version , ) def decorator ( func : F ) -> F : assert_function_is_not_finalised ( func ) store = get_function_metadata_store ( func ) store . model_parameter_names . append ( model_kwarg_name ) @wraps ( func ) def wrapper ( * args : List [ Any ], ** kwargs : Dict [ str , Any ]) -> Any : tracing_context = TracingContext . get_current_tracing_context () if tracing_context : tracing_context . log_model ( Model ( key = key , version = actual_version )) return func ( * args , ** kwargs , ** { model_kwarg_name : model }) return cast ( F , wrapper ) return decorator","title":"use_model()"},{"location":"reference/#great_ai.parameter","text":"Control the validation and logging of function parameters. Examples: >>> @parameter ( 'a' ) ... def my_function ( a : int ): ... return a + 2 >>> my_function ( 4 ) 6 >>> my_function ( '3' ) Traceback (most recent call last): ... TypeError : type of a must be int; got str instead >>> @parameter ( 'positive_a' , validate = lambda v : v > 0 ) ... def my_function ( positive_a : int ): ... return a + 2 >>> my_function ( - 1 ) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError : ... Parameters: Name Type Description Default parameter_name str Name of parameter to consider required validate Callable [[ Any ], bool ] Optional validate to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. lambda _: True disable_logging bool Do not save the value in any active TracingContext. False Returns: Type Description Callable [[ F ], F ] A decorator for argument validation. Source code in great_ai/parameters/parameter.py 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 def parameter ( parameter_name : str , * , validate : Callable [[ Any ], bool ] = lambda _ : True , disable_logging : bool = False , ) -> Callable [[ F ], F ]: \"\"\"Control the validation and logging of function parameters. Examples: >>> @parameter('a') ... def my_function(a: int): ... return a + 2 >>> my_function(4) 6 >>> my_function('3') Traceback (most recent call last): ... TypeError: type of a must be int; got str instead >>> @parameter('positive_a', validate=lambda v: v > 0) ... def my_function(positive_a: int): ... return a + 2 >>> my_function(-1) Traceback (most recent call last): ... great_ai.errors.argument_validation_error.ArgumentValidationError: ... Args: parameter_name: Name of parameter to consider validate: Optional validate to run against the concrete argument. ArgumentValidationError is thrown when the return value is False. disable_logging: Do not save the value in any active TracingContext. Returns: A decorator for argument validation. \"\"\" def decorator ( func : F ) -> F : get_function_metadata_store ( func ) . input_parameter_names . append ( parameter_name ) assert_function_is_not_finalised ( func ) actual_name = f \"arg: { parameter_name } \" @wraps ( func ) def wrapper ( * args : Any , ** kwargs : Dict [ str , Any ]) -> Any : arguments = get_arguments ( func , args , kwargs ) argument = arguments . get ( parameter_name ) expected_type = func . __annotations__ . get ( parameter_name ) if expected_type is not None : check_type ( parameter_name , argument , expected_type ) if not validate ( argument ): raise ArgumentValidationError ( f \"\"\"Argument { parameter_name } in { func . __name__ } did not pass validation\"\"\" ) context = TracingContext . get_current_tracing_context () if context and not disable_logging : context . log_value ( name = f \" { actual_name } :value\" , value = argument ) if isinstance ( argument , str ): context . log_value ( name = f \" { actual_name } :length\" , value = len ( argument )) return func ( * args , ** kwargs ) return cast ( F , wrapper ) return decorator","title":"parameter()"},{"location":"reference/#great_ai.log_metric","text":"Source code in great_ai/parameters/log_metric.py 8 9 10 11 12 13 14 15 def log_metric ( argument_name : str , value : Any ) -> None : tracing_context = TracingContext . get_current_tracing_context () caller = inspect . stack ()[ 1 ] . function actual_name = f \"metric: { caller } : { argument_name } \" if tracing_context : tracing_context . log_value ( name = actual_name , value = value ) get_context () . logger . info ( f \" { actual_name } = { value } \" )","title":"log_metric()"},{"location":"reference/#remote-calls","text":"","title":"Remote calls"},{"location":"reference/#great_ai.call_remote_great_ai","text":"Communicate with a GreatAI object through an HTTP request. Wrapper over call_remote_great_ai_async making it synchronous. For more info, see call_remote_great_ai_async . Source code in great_ai/remote/call_remote_great_ai.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 def call_remote_great_ai ( base_uri : str , data : Mapping [ str , Any ], retry_count : int = 4 , timeout_in_seconds : Optional [ int ] = 300 , model_class : Optional [ Type [ T ]] = None , ) -> Trace [ T ]: \"\"\"Communicate with a GreatAI object through an HTTP request. Wrapper over `call_remote_great_ai_async` making it synchronous. For more info, see `call_remote_great_ai_async`. \"\"\" try : asyncio . get_running_loop () raise Exception ( f \"Already running in an event loop, you have to call ` { call_remote_great_ai_async . __name__ } `\" ) except RuntimeError : pass future = call_remote_great_ai_async ( base_uri = base_uri , data = data , retry_count = retry_count , timeout_in_seconds = timeout_in_seconds , model_class = model_class , ) return asyncio . run ( future )","title":"call_remote_great_ai()"},{"location":"reference/#great_ai.call_remote_great_ai_async","text":"Communicate with a GreatAI object through an HTTP request. Send a POST request using httpx to implement a remote call. Error-handling and retries are provided by httpx. The return value is inflated into a Trace. If model_class is specified, the original output is deserialised. Parameters: Name Type Description Default base_uri str Address of the remote instance, example: 'http://localhost:6060' required data Mapping [ str , Any ] The input sent as a json to the remote instance. required retry_count int Retry on any HTTP communication failure. 4 timeout_in_seconds Optional [ int ] Overall permissable max length of the request. None means no timeout. 300 model_class Optional [ Type [ T ]] A subtype of BaseModel to be used for deserialising the .output of the trace. None Source code in great_ai/remote/call_remote_great_ai_async.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 async def call_remote_great_ai_async ( base_uri : str , data : Mapping [ str , Any ], retry_count : int = 4 , timeout_in_seconds : Optional [ int ] = 300 , model_class : Optional [ Type [ T ]] = None , ) -> Trace [ T ]: \"\"\"Communicate with a GreatAI object through an HTTP request. Send a POST request using [httpx](https://www.python-httpx.org/) to implement a remote call. Error-handling and retries are provided by httpx. The return value is inflated into a Trace. If `model_class` is specified, the original output is deserialised. Args: base_uri: Address of the remote instance, example: 'http://localhost:6060' data: The input sent as a json to the remote instance. retry_count: Retry on any HTTP communication failure. timeout_in_seconds: Overall permissable max length of the request. `None` means no timeout. model_class: A subtype of BaseModel to be used for deserialising the `.output` of the trace. \"\"\" if base_uri . endswith ( \"/\" ): base_uri = base_uri [: - 1 ] if not base_uri . endswith ( \"/predict\" ): base_uri = f \" { base_uri } /predict\" transport = httpx . AsyncHTTPTransport ( retries = retry_count ) try : async with httpx . AsyncClient ( transport = transport , timeout = timeout_in_seconds ) as client : response = await client . post ( base_uri , json = data ) try : response . raise_for_status () except Exception : raise RemoteCallError ( f \"Unexpected status code, reason: { response . text } \" ) except Exception as e : raise RemoteCallError from e try : trace = response . json () except Exception : raise RemoteCallError ( f \"JSON parsing failed { response . text } \" , ) try : if model_class is not None : trace [ \"output\" ] = model_class . parse_obj ( trace [ \"output\" ]) return Trace . parse_obj ( trace ) except Exception : raise RemoteCallError ( \"Could not parse Trace\" )","title":"call_remote_great_ai_async()"},{"location":"reference/#ground-truth","text":"","title":"Ground-truth"},{"location":"reference/#great_ai.add_ground_truth","text":"Add training data (with optional train-test splitting). Add and tag data-points, wrap them into traces. The inputs are available via the .input property, while expected_outputs under both the .output and .feedback properties. All generated traces are tagged with ground_truth by default. Additional tags can be also provided. Using the split_ratio arguments, tags can be given randomly with a user-defined distribution. Only the ratio of the splits matter, they don't have to add up to 1. Examples: >>> add_ground_truth ( ... [ 1 , 2 , 3 ], ... [ 'odd' , 'even' , 'odd' ], ... tags = 'my_tag' , ... train_split_ratio = 1 , ... test_split_ratio = 1 , ... validation_split_ratio = 0.5 , ... ) >>> add_ground_truth ( ... [ 1 , 2 ], ... [ 'odd' , 'even' , 'odd' ], ... tags = 'my_tag' , ... train_split_ratio = 1 , ... test_split_ratio = 1 , ... validation_split_ratio = 0.5 , ... ) Traceback (most recent call last): ... AssertionError : The length of the inputs and expected_outputs must be equal Parameters: Name Type Description Default inputs Iterable [ Any ] The inputs. (X in scikit-learn) required expected_outputs Iterable [ T ] The ground-truth values corresponding to the inputs. (y in scikit-learn) required tags Union [ List [ str ], str ] A single tag or a list of tags to append to each generated trace's tags. [] train_split_ratio float The probability-weight of giving each trace the train tag. 1 test_split_ratio float The probability-weight of giving each trace the test tag. 0 validation_split_ratio float The probability-weight of giving each trace the validation tag. 0 Source code in great_ai/tracing/add_ground_truth.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 def add_ground_truth ( inputs : Iterable [ Any ], expected_outputs : Iterable [ T ], * , tags : Union [ List [ str ], str ] = [], train_split_ratio : float = 1 , test_split_ratio : float = 0 , validation_split_ratio : float = 0 ) -> None : \"\"\"Add training data (with optional train-test splitting). Add and tag data-points, wrap them into traces. The `inputs` are available via the `.input` property, while `expected_outputs` under both the `.output` and `.feedback` properties. All generated traces are tagged with `ground_truth` by default. Additional tags can be also provided. Using the `split_ratio` arguments, tags can be given randomly with a user-defined distribution. Only the ratio of the splits matter, they don't have to add up to 1. Examples: >>> add_ground_truth( ... [1, 2, 3], ... ['odd', 'even', 'odd'], ... tags='my_tag', ... train_split_ratio=1, ... test_split_ratio=1, ... validation_split_ratio=0.5, ... ) >>> add_ground_truth( ... [1, 2], ... ['odd', 'even', 'odd'], ... tags='my_tag', ... train_split_ratio=1, ... test_split_ratio=1, ... validation_split_ratio=0.5, ... ) Traceback (most recent call last): ... AssertionError: The length of the inputs and expected_outputs must be equal Args: inputs: The inputs. (X in scikit-learn) expected_outputs: The ground-truth values corresponding to the inputs. (y in scikit-learn) tags: A single tag or a list of tags to append to each generated trace's tags. train_split_ratio: The probability-weight of giving each trace the `train` tag. test_split_ratio: The probability-weight of giving each trace the `test` tag. validation_split_ratio: The probability-weight of giving each trace the `validation` tag. \"\"\" inputs = list ( inputs ) expected_outputs = list ( expected_outputs ) assert len ( inputs ) == len ( expected_outputs ), \"The length of the inputs and expected_outputs must be equal\" tags = tags if isinstance ( tags , list ) else [ tags ] sum_ratio = train_split_ratio + test_split_ratio + validation_split_ratio assert sum_ratio > 0 , \"The sum of the split ratios must be a positive number\" train_split_ratio /= sum_ratio test_split_ratio /= sum_ratio validation_split_ratio /= sum_ratio values = list ( zip ( inputs , expected_outputs )) shuffle ( values ) split_tags = ( [ TRAIN_SPLIT_TAG_NAME ] * ceil ( train_split_ratio * len ( inputs )) + [ TEST_SPLIT_TAG_NAME ] * ceil ( test_split_ratio * len ( inputs )) + [ VALIDATION_SPLIT_TAG_NAME ] * ceil ( validation_split_ratio * len ( inputs )) ) shuffle ( split_tags ) created = datetime . utcnow () . isoformat () traces = [ cast ( Trace [ T ], Trace ( # avoid ValueError: \"Trace\" object has no field \"__orig_class__\" trace_id = str ( uuid4 ()), created = created , original_execution_time_ms = 0 , logged_values = X if isinstance ( X , dict ) else { \"input\" : X }, models = [], output = y , feedback = y , exception = None , tags = [ GROUND_TRUTH_TAG_NAME , split_tag , * tags ], ), ) for (( X , y ), split_tag ) in zip ( values , split_tags ) ] get_context () . tracing_database . save_batch ( traces )","title":"add_ground_truth()"},{"location":"reference/#great_ai.query_ground_truth","text":"Return training samples. Combines, filters, and returns data-points that have been either added by add_ground_truth or were the result of a prediction after which their trace got a feedback through the RESP API-s /traces/{trace_id}/feedback endpoint (end-to-end feedback). Filtering can be used to only return points matching all given tags (or the single given tag) and by time of creation. Examples: >>> query_ground_truth () [...] Parameters: Name Type Description Default conjunctive_tags Union [ List [ str ], str ] Single tag or a list of tags which the returned traces have to match. The relationship between the tags is conjunctive (AND). [] since Optional [ datetime ] Only return traces created after the given timestamp. None means no filtering. None until Optional [ datetime ] Only return traces created before the given timestamp. None means no filtering. None return_max_count Optional [ int ] Return at-most this many traces. (take, limit) None Source code in great_ai/tracing/query_ground_truth.py 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 def query_ground_truth ( conjunctive_tags : Union [ List [ str ], str ] = [], * , since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , return_max_count : Optional [ int ] = None ) -> List [ Trace ]: \"\"\"Return training samples. Combines, filters, and returns data-points that have been either added by `add_ground_truth` or were the result of a prediction after which their trace got a feedback through the RESP API-s `/traces/{trace_id}/feedback` endpoint (end-to-end feedback). Filtering can be used to only return points matching all given tags (or the single given tag) and by time of creation. Examples: >>> query_ground_truth() [...] Args: conjunctive_tags: Single tag or a list of tags which the returned traces have to match. The relationship between the tags is conjunctive (AND). since: Only return traces created after the given timestamp. `None` means no filtering. until: Only return traces created before the given timestamp. `None` means no filtering. return_max_count: Return at-most this many traces. (take, limit) \"\"\" tags = ( conjunctive_tags if isinstance ( conjunctive_tags , list ) else [ conjunctive_tags ] ) db = get_context () . tracing_database items , length = db . query ( conjunctive_tags = tags , since = since , until = until , take = return_max_count , has_feedback = True , ) return items","title":"query_ground_truth()"},{"location":"reference/#great_ai.delete_ground_truth","text":"Delete traces matching the given criteria. Takes the same arguments as query_ground_truth but instead of returning them, it simply deletes them. You can rely on the efficiency of the delete's implementation. Examples: >>> delete_ground_truth ([ 'train' , 'test' , 'validation' ]) Parameters: Name Type Description Default conjunctive_tags Union [ List [ str ], str ] Single tag or a list of tags which the deleted traces have to match. The relationship between the tags is conjunctive (AND). [] since Optional [ datetime ] Only delete traces created after the given timestamp. None means no filtering. None until Optional [ datetime ] Only delete traces created before the given timestamp. None means no filtering. None Source code in great_ai/tracing/delete_ground_truth.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 def delete_ground_truth ( conjunctive_tags : Union [ List [ str ], str ] = [], * , since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , ) -> None : \"\"\"Delete traces matching the given criteria. Takes the same arguments as `query_ground_truth` but instead of returning them, it simply deletes them. You can rely on the efficiency of the delete's implementation. Examples: >>> delete_ground_truth(['train', 'test', 'validation']) Args: conjunctive_tags: Single tag or a list of tags which the deleted traces have to match. The relationship between the tags is conjunctive (AND). since: Only delete traces created after the given timestamp. `None` means no filtering. until: Only delete traces created before the given timestamp. `None` means no filtering. \"\"\" tags = ( conjunctive_tags if isinstance ( conjunctive_tags , list ) else [ conjunctive_tags ] ) db = get_context () . tracing_database items , length = db . query ( conjunctive_tags = tags , until = until , since = since , has_feedback = True ) db . delete_batch ([ i . trace_id for i in items ])","title":"delete_ground_truth()"},{"location":"reference/#tracing-databases","text":"","title":"Tracing databases"},{"location":"reference/#great_ai.TracingDatabaseDriver","text":"Bases: ABC Interface expected from a database to be used for storing traces. Source code in great_ai/persistence/tracing_database_driver.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 class TracingDatabaseDriver ( ABC ): \"\"\"Interface expected from a database to be used for storing traces.\"\"\" is_production_ready : bool initialized : bool = False @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()}) @classmethod def configure_credentials ( cls , ) -> None : cls . initialized = True @abstractmethod def save ( self , document : Trace ) -> str : pass @abstractmethod def save_batch ( self , documents : List [ Trace ], ) -> List [ str ]: pass @abstractmethod def get ( self , id : str ) -> Optional [ Trace ]: pass @abstractmethod def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], until : Optional [ datetime ] = None , since : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [] ) -> Tuple [ List [ Trace ], int ]: pass @abstractmethod def update ( self , id : str , new_version : Trace ) -> None : pass @abstractmethod def delete ( self , id : str ) -> None : pass @abstractmethod def delete_batch ( self , ids : List [ str ], ) -> None : pass","title":"TracingDatabaseDriver"},{"location":"reference/#great_ai.MongoDbDriver","text":"Bases: TracingDatabaseDriver TracingDatabaseDriver implementation using MongoDB as a backend. A production-ready database driver suitable for efficiently handling semi-structured data. Checkout MongoDB Atlas for a hosted MongoDB solution. Source code in great_ai/persistence/mongodb_driver.py 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 class MongoDbDriver ( TracingDatabaseDriver ): \"\"\"TracingDatabaseDriver implementation using MongoDB as a backend. A production-ready database driver suitable for efficiently handling semi-structured data. Checkout [MongoDB Atlas](https://www.mongodb.com/cloud/atlas/register) for a hosted MongoDB solution. \"\"\" is_production_ready = True mongo_connection_string : str mongo_database : str def __init__ ( self ) -> None : super () . __init__ () if self . mongo_connection_string is None or self . mongo_database is None : raise ValueError ( \"Please configure the MongoDB access options by calling \" \"MongoDbDriver.configure_credentials\" ) with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . create_index ( [( \"tags\" , ASCENDING ), ( \"created\" , DESCENDING )], background = True ) @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : \"\"\"Configure the connection details for MongoDB. Args: mongo_connection_string: For example: 'mongodb://my_user:my_pass@localhost:27017' mongo_database: Name of the database to use. If doesn't exist, it is created and initialised. \"\"\" cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () def save ( self , trace : Trace ) -> str : serialized = trace . to_flat_dict () serialized [ \"_id\" ] = trace . trace_id with MongoClient [ Any ]( self . mongo_connection_string ) as client : return client [ self . mongo_database ] . traces . insert_one ( serialized ) . inserted_id def save_batch ( self , documents : List [ Trace ]) -> List [ str ]: serialized = [ d . to_flat_dict () for d in documents ] for s in serialized : s [ \"_id\" ] = s [ \"trace_id\" ] with MongoClient [ Any ]( self . mongo_connection_string ) as client : return ( client [ self . mongo_database ] . traces . insert_many ( serialized , ordered = False ) . inserted_ids ) def get ( self , id : str ) -> Optional [ Trace ]: with MongoClient [ Any ]( self . mongo_connection_string ) as client : value = client [ self . mongo_database ] . traces . find_one ( id ) if value : value = Trace . parse_obj ( value ) return value def _get_operator ( self , filter : Filter ) -> str : if filter . operator == \"contains\" and not isinstance ( filter . value , str ): return operator_mapping [ \"=\" ] return operator_mapping [ filter . operator ] def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [], ) -> Tuple [ List [ Trace ], int ]: query : Dict [ str , Any ] = { \"filter\" : {}, } and_query : List [ Dict [ str , Any ]] = [] and_query . extend ({ \"tags\" : tag } for tag in conjunctive_tags ) and_query . extend ( { f . property : { self . _get_operator ( f ): f . value }} for f in conjunctive_filters ) if not and_query : and_query . append ({}) if since : and_query . append ({ \"created\" : { \"$gte\" : since }}) if until : and_query . append ({ \"created\" : { \"$lte\" : until }}) if has_feedback is not None : and_query . append ( { \"feedback\" : { \"$ne\" : None }} if has_feedback else { \"feedback\" : None } ) query [ \"filter\" ][ \"$and\" ] = and_query with MongoClient [ Any ]( self . mongo_connection_string ) as client : count = client [ self . mongo_database ] . traces . count_documents ( ** query ) if skip : query [ \"skip\" ] = skip if take : query [ \"limit\" ] = take query [ \"sort\" ] = [ ( col . column_id , 1 if col . direction == \"asc\" else - 1 ) for col in sort_by ] with client [ self . mongo_database ] . traces . find ( ** query ) as cursor : documents = [ Trace [ Any ] . parse_obj ( t ) for t in cursor ] return documents , count def update ( self , id : str , new_version : Trace ) -> None : serialized = new_version . to_flat_dict () serialized [ \"_id\" ] = new_version . trace_id with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . update_one ({ \"_id\" : id }, serialized ) def delete ( self , id : str ) -> None : with MongoClient [ Any ]( self . mongo_connection_string ) as client : client [ self . mongo_database ] . traces . delete_one ({ \"_id\" : id }) def delete_batch ( self , ids : List [ str ]) -> None : with MongoClient [ Any ]( self . mongo_connection_string ) as client : for c in chunk ( ids , chunk_size = 10000 ): # avoid: 'delete' command document too large delete_filter = { \"_id\" : { \"$in\" : c }} client [ self . mongo_database ] . traces . delete_many ( delete_filter )","title":"MongoDbDriver"},{"location":"reference/#great_ai.persistence.mongodb_driver.MongoDbDriver.configure_credentials","text":"Configure the connection details for MongoDB. Parameters: Name Type Description Default mongo_connection_string str For example: 'mongodb://my_user:my_pass@localhost:27017' required mongo_database str Name of the database to use. If doesn't exist, it is created and initialised. required Source code in great_ai/persistence/mongodb_driver.py 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : \"\"\"Configure the connection details for MongoDB. Args: mongo_connection_string: For example: 'mongodb://my_user:my_pass@localhost:27017' mongo_database: Name of the database to use. If doesn't exist, it is created and initialised. \"\"\" cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials ()","title":"configure_credentials()"},{"location":"reference/#great_ai.ParallelTinyDbDriver","text":"Bases: TracingDatabaseDriver TracingDatabaseDriver with TinyDB as a backend. Saves the database as a JSON into a single file. Highly inefficient on inserting, not advised for production use. A multiprocessing lock protects the database file to avoid parallelisation issues. Source code in great_ai/persistence/parallel_tinydb_driver.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 class ParallelTinyDbDriver ( TracingDatabaseDriver ): \"\"\"TracingDatabaseDriver with TinyDB as a backend. Saves the database as a JSON into a single file. Highly inefficient on inserting, not advised for production use. A multiprocessing lock protects the database file to avoid parallelisation issues. \"\"\" is_production_ready = False path_to_db = Path ( DEFAULT_TRACING_DB_FILENAME ) def save ( self , trace : Trace ) -> str : return self . _safe_execute ( lambda db : db . insert ( trace . dict ())) def save_batch ( self , documents : List [ Trace ]) -> List [ str ]: traces = [ d . dict () for d in documents ] return self . _safe_execute ( lambda db : db . insert_multiple ( traces )) def get ( self , id : str ) -> Optional [ Trace ]: value = self . _safe_execute ( lambda db : db . get ( lambda d : d [ \"trace_id\" ] == id )) if value : value = Trace . parse_obj ( value ) return value def query ( self , * , skip : int = 0 , take : Optional [ int ] = None , conjunctive_filters : Sequence [ Filter ] = [], conjunctive_tags : Sequence [ str ] = [], since : Optional [ datetime ] = None , until : Optional [ datetime ] = None , has_feedback : Optional [ bool ] = None , sort_by : Sequence [ SortBy ] = [] ) -> Tuple [ List [ Trace ], int ]: def does_match ( d : Dict [ str , Any ]) -> bool : return ( not set ( conjunctive_tags ) - set ( d [ \"tags\" ]) and ( since is None or datetime . fromisoformat ( d [ \"created\" ]) >= since ) and ( until is None or datetime . fromisoformat ( d [ \"created\" ]) <= until ) and ( has_feedback is None or has_feedback == ( d [ \"feedback\" ] is not None ) ) ) documents : List [ Trace ] = [ Trace . parse_obj ( t ) for t in self . _safe_execute ( lambda db : db . search ( does_match )) ] if not documents : return [], 0 df = pd . DataFrame ([ d . to_flat_dict () for d in documents ]) for f in conjunctive_filters : operator = f . operator . lower () if operator in operator_mapping : df = df . loc [ getattr ( df [ f . property ], operator_mapping [ f . operator ])( f . value ) ] elif operator == \"contains\" : df = df . loc [ df [ f . property ] . str . contains ( f . value , case = False )] if sort_by : df . sort_values ( [ col . column_id for col in sort_by ], ascending = [ col . direction == \"asc\" for col in sort_by ], inplace = True , ) count = len ( df ) result = df . iloc [ skip :] if take is None else df . iloc [ skip : skip + take ] return [ next ( d for d in documents if d . trace_id == trace_id ) for trace_id in result [ \"trace_id\" ] ], count def update ( self , id : str , new_version : Trace ) -> None : self . _safe_execute ( lambda db : db . update ( new_version . dict (), lambda d : d [ \"trace_id\" ] == id ) ) def delete ( self , id : str ) -> None : self . _safe_execute ( lambda db : db . remove ( lambda d : d [ \"trace_id\" ] == id )) def delete_batch ( self , ids : List [ str ]) -> None : for i in ids : self . delete ( i ) def _safe_execute ( self , func : Callable [[ TinyDB ], Any ]) -> Any : with lock : with TinyDB ( self . path_to_db ) as db : return func ( db )","title":"ParallelTinyDbDriver"},{"location":"reference/large-file/","text":"LargeFile # from great_ai.large_file import * LargeFileLocal # Bases: LargeFileBase Source code in great_ai/large_file/large_file/large_file_local.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 class LargeFileLocal ( LargeFileBase ): def __init__ ( self , name : str , mode : str = \"r\" , * , buffering : int = - 1 , encoding : Optional [ str ] = None , errors : Optional [ str ] = None , newline : Optional [ str ] = None , version : Optional [ int ] = None , keep_last_n : Optional [ int ] = None , ): super () . __init__ ( name , mode , buffering = buffering , encoding = encoding , errors = errors , newline = newline , version = version , keep_last_n = keep_last_n , cache_only_mode = True , ) super () . configure_credentials () def _find_remote_instances ( self ) -> List [ DataInstance ]: return [] def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : raise NotImplementedError () def _upload ( self , local_path : Path , hide_progress : bool ) -> None : pass # the \"upload\" is already done py the parent's caching mechanism def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version (keep_last_n= { self . _keep_last_n } ): { i . remote_path } \" ) i . remote_path . unlink () LargeFileS3 # Bases: LargeFileBase Store large files in S3. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test ') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` Source code in great_ai/large_file/large_file/large_file_s3.py 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 class LargeFileS3 ( LargeFileBase ): \"\"\" Store large files in S3. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test\\n') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` \"\"\" region_name = None access_key_id = None secret_access_key = None bucket_name = None endpoint_url = None @classmethod def configure_credentials ( # type: ignore cls , * , aws_region_name : str , aws_access_key_id : str , aws_secret_access_key : str , large_files_bucket_name : str , aws_endpoint_url : Optional [ str ] = None , ** _ : Any , ) -> None : cls . region_name = aws_region_name cls . access_key_id = aws_access_key_id cls . secret_access_key = aws_secret_access_key cls . bucket_name = large_files_bucket_name cls . endpoint_url = aws_endpoint_url super () . configure_credentials () @cached_property def _client ( self ) -> boto3 . client : if ( self . region_name is None or self . access_key_id is None or self . secret_access_key is None or self . bucket_name is None ): raise ValueError ( \"Please configure the S3 access options by calling LargeFileS3.configure_credentials or set offline_mode=True in the constructor.\" ) return boto3 . client ( \"s3\" , aws_access_key_id = self . access_key_id , aws_secret_access_key = self . secret_access_key , region_name = self . region_name , endpoint_url = self . endpoint_url , ) def _find_remote_instances ( self ) -> List [ DataInstance ]: logger . debug ( f \"Fetching S3 versions of { self . _name } \" ) found_objects = self . _client . list_objects_v2 ( Bucket = self . bucket_name , Prefix = self . _name ) return ( [ DataInstance ( name = o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ 0 ], version = int ( o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = o [ \"Key\" ], ) for o in found_objects [ \"Contents\" ] if o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ 0 ] == self . _name ] if \"Contents\" in found_objects else [] ) def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Downloading { remote_path } from S3\" ) size = self . _client . head_object ( Bucket = self . bucket_name , Key = remote_path )[ \"ContentLength\" ] self . _client . download_file ( Bucket = self . bucket_name , Key = remote_path , Filename = str ( local_path ), Callback = None if hide_progress else DownloadProgressBar ( name = str ( remote_path ), size = size , logger = logger ), ) def _upload ( self , local_path : Path , hide_progress : bool ) -> None : key = f \" { self . _name } / { self . version } \" logger . info ( f \"Uploading { self . _local_name } to S3 as { key } \" ) self . _client . upload_file ( Filename = str ( local_path ), Bucket = self . bucket_name , Key = key , Callback = None if hide_progress else UploadProgressBar ( path = local_path , logger = logger ), ) def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version from S3 (keep_last_n= { self . _keep_last_n } ): { i . remote_path } \" ) self . _client . delete_object ( Bucket = self . bucket_name , Key = i . remote_path ) LargeFileMongo # Bases: LargeFileBase Source code in great_ai/large_file/large_file/large_file_mongo.py 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 class LargeFileMongo ( LargeFileBase ): mongo_connection_string = None mongo_database = None @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () @cached_property def _client ( self ) -> GridFSBucket : if self . mongo_connection_string is None or self . mongo_database is None : raise ValueError ( \"Please configure the MongoDB access options by calling LargeFileMongo.configure_credentials or set offline_mode=True in the constructor.\" ) db : Database = MongoClient ( self . mongo_connection_string )[ self . mongo_database ] return GridFSBucket ( db ) def _find_remote_instances ( self ) -> List [ DataInstance ]: logger . debug ( f \"Fetching Mongo (GridFS) versions of { self . _name } \" ) return [ DataInstance ( name = MONGO_NAME_VERSION_SEPARATOR . join ( f . name . split ( MONGO_NAME_VERSION_SEPARATOR )[: - 1 ] ), version = int ( f . name . split ( MONGO_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = ( f . _id , f . length ), origin = \"mongodb\" , ) for f in self . _client . find ( { \"filename\" : re . compile ( re . escape ( self . _name + MONGO_NAME_VERSION_SEPARATOR ) + \".*\" ) } ) ] def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Downloading { remote_path [ 0 ] } from Mongo (GridFS)\" ) progress = ( DownloadProgressBar ( name = str ( remote_path [ 0 ]), size = remote_path [ 1 ], logger = logger ) if not hide_progress else None ) with self . _client . open_download_stream ( remote_path [ 0 ]) as stream : with open ( local_path , \"wb\" ) as f : while True : content = stream . read ( DEFAULT_CHUNK_SIZE ) f . write ( content ) if progress : progress ( len ( content )) if len ( content ) < DEFAULT_CHUNK_SIZE : break def _upload ( self , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Uploading { local_path } to Mongo (GridFS)\" ) progress = ( UploadProgressBar ( path = local_path , logger = logger ) if not hide_progress else None ) with self . _client . open_upload_stream ( f \" { self . _name }{ MONGO_NAME_VERSION_SEPARATOR }{ self . version } \" ) as stream : with open ( local_path , \"rb\" ) as f : while True : content = f . read ( DEFAULT_CHUNK_SIZE ) stream . write ( content ) if progress : progress ( len ( content )) if len ( content ) < DEFAULT_CHUNK_SIZE : break def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version from MongoDB (GridFS) (keep_last_n= { self . _keep_last_n } ): { i . name }{ MONGO_NAME_VERSION_SEPARATOR }{ i . version } \" ) self . _client . delete ( i . remote_path [ 0 ]) LargeFileBase # Bases: ABC Store large files remotely. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test ') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` Source code in great_ai/large_file/large_file/large_file_base.py 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 class LargeFileBase ( ABC ): \"\"\" Store large files remotely. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test\\n') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` \"\"\" initialized = False cache_path = Path ( \".cache\" ) max_cache_size : Optional [ str ] = \"30GB\" def __init__ ( self , name : str , mode : str = \"r\" , * , buffering : int = - 1 , encoding : Optional [ str ] = None , errors : Optional [ str ] = None , newline : Optional [ str ] = None , version : Optional [ int ] = None , keep_last_n : Optional [ int ] = None , cache_only_mode : bool = False , ): self . _name = name self . _version = version self . _mode = mode self . _keep_last_n = keep_last_n self . _cache_only_mode = cache_only_mode self . _buffering = buffering self . _encoding = encoding self . _errors = errors self . _newline = newline LargeFileBase . cache_path . mkdir ( parents = True , exist_ok = True ) self . _find_instances () self . _check_mode_and_set_version () @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()}) @classmethod def configure_credentials ( cls , ** kwargs : str ) -> None : cls . initialized = True def __enter__ ( self ) -> IO : self . _file : IO [ Any ] = ( tempfile . NamedTemporaryFile ( mode = self . _mode , buffering = self . _buffering , encoding = self . _encoding , newline = self . _newline , errors = self . _errors , delete = False , prefix = \"large_file-\" , ) if \"w\" in self . _mode else open ( self . get (), mode = self . _mode , buffering = self . _buffering , encoding = self . _encoding , newline = self . _newline , errors = self . _errors , ) ) return self . _file def __exit__ ( self , type : Optional [ Type [ BaseException ]], exc : Optional [ BaseException ], traceback : Optional [ TracebackType ], ) -> Literal [ False ]: self . _file . close () if type is None : if \"w\" in self . _mode : self . push ( Path ( self . _file . name )) os . unlink ( self . _file . name ) else : logger . exception ( \"Could not finish operation.\" ) return False @property def _local_name ( self ) -> str : return f \" { self . _name }{ CACHE_NAME_VERSION_SEPARATOR }{ self . version } \" @property def version ( self ) -> int : return cast ( int , self . _version ) @lru_cache ( 1 ) def get ( self , hide_progress : bool = False ) -> Path : remote_path = next ( i . remote_path for i in self . _instances if i . version == self . _version ) destination = self . cache_path / self . _local_name if not destination . exists (): logger . info ( f \"File { self . _local_name } does not exist locally\" ) with tempfile . TemporaryDirectory () as tmp : local_root_path = Path ( tmp ) tmp_file_archive = ( local_root_path / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _download ( remote_path , tmp_file_archive , hide_progress = hide_progress ) logger . info ( f \"Decompressing { self . _local_name } \" ) shutil . unpack_archive ( str ( tmp_file_archive ), tmp , COMPRESSION_ALGORITHM ) shutil . move ( str ( local_root_path / self . _local_name ), str ( destination )) else : logger . info ( f \"File { self . _local_name } found in cache\" ) return destination def push ( self , path : Union [ Path , str ], hide_progress : bool = False ) -> None : if isinstance ( path , str ): path = Path ( path ) with tempfile . TemporaryDirectory () as tmp : if path . is_file (): logger . info ( f \"Copying file for { self . _local_name } \" ) copy : Any = shutil . copy else : logger . info ( f \"Copying directory for { self . _local_name } \" ) copy = shutil . copytree try : # Make local copy in the cache shutil . rmtree ( self . cache_path / self . _local_name , ignore_errors = True ) copy ( str ( path ), str ( self . cache_path / self . _local_name )) except shutil . SameFileError : pass # No worries copy ( str ( path ), str ( Path ( tmp ) / self . _local_name )) with tempfile . TemporaryDirectory () as tmp2 : # A directory has to be zipped and it cannot contain the output of the zipping logger . info ( f \"Compressing { self . _local_name } \" ) shutil . make_archive ( str ( Path ( tmp2 ) / self . _local_name ), COMPRESSION_ALGORITHM , tmp , ) file_to_be_uploaded = ( Path ( tmp2 ) / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _upload ( file_to_be_uploaded , hide_progress = hide_progress ) self . clean_up () def delete ( self ) -> None : self . _keep_last_n = 0 self . _delete_old_remote_versions () def _find_instances ( self ) -> None : if self . _cache_only_mode : self . _instances = self . _find_instances_from_cache () else : self . _instances = self . _find_remote_instances () self . _instances = sorted ( self . _instances , key = lambda i : i . version ) def _find_instances_from_cache ( self ) -> List [ DataInstance ]: logger . info ( f \"Fetching cached versions of { self . _name } \" ) candidates = [ DataInstance ( name = CACHE_NAME_VERSION_SEPARATOR . join ( f . name . split ( CACHE_NAME_VERSION_SEPARATOR )[: - 1 ] ), version = int ( f . name . split ( CACHE_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = f , ) for f in self . cache_path . glob ( f \" { self . _name }{ CACHE_NAME_VERSION_SEPARATOR } *\" ) ] return [ c for c in candidates if c . name == self . _name ] def _check_mode_and_set_version ( self ) -> None : if \"+\" in self . _mode : raise ValueError ( f \"File mode ` { self . _mode } ` is not allowed3, remove the `+`.\" ) if \"w\" in self . _mode : if self . _version is not None : raise ValueError ( \"Providing a version is not allowed in write mode.\" ) self . _version = self . _instances [ - 1 ] . version + 1 if self . _instances else 0 elif \"r\" in self . _mode : if not self . _instances : raise FileNotFoundError ( f \"File { self . _name } not found. No versions are available.\" ) if self . _version is None : self . _version = self . _instances [ - 1 ] . version logger . info ( f \"Latest version of { self . _name } is { self . _version } \" + f \"(from versions: { self . versions_pretty } )\" ) elif self . _version not in [ i . version for i in self . _instances ]: raise FileNotFoundError ( f \"File { self . _name } not found with version { self . _version } . \" + f \"(from versions: { self . versions_pretty } )\" ) else : raise ValueError ( \"Unsupported file mode.\" ) @property def versions_pretty ( self ) -> str : return \", \" . join (( str ( i . version ) for i in self . _instances )) def clean_up ( self ) -> None : self . _delete_old_remote_versions () self . _prune_cache () def _prune_cache ( self ) -> None : self . cache_path . mkdir ( parents = True , exist_ok = True ) if self . max_cache_size is None : return allowed_size = human_readable_to_byte ( self . max_cache_size ) assert allowed_size >= 0 least_recently_read = sorted ( [ f for f in self . cache_path . glob ( \"*\" )], key = lambda f : f . stat () . st_atime ) while sum ( os . path . getsize ( f ) for f in least_recently_read ) > allowed_size : file = least_recently_read . pop ( 0 ) logger . info ( f \"Deleting file from cache to meet quota (max_cache_size= { self . max_cache_size } ): { file } \" ) os . unlink ( file ) @abstractmethod def _find_remote_instances ( self ) -> List [ DataInstance ]: pass @abstractmethod def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : pass @abstractmethod def _upload ( self , local_path : Path , hide_progress : bool ) -> None : pass @abstractmethod def _delete_old_remote_versions ( self ) -> None : pass","title":"LargeFile"},{"location":"reference/large-file/#largefile","text":"from great_ai.large_file import *","title":"LargeFile"},{"location":"reference/large-file/#great_ai.large_file.LargeFileLocal","text":"Bases: LargeFileBase Source code in great_ai/large_file/large_file/large_file_local.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 class LargeFileLocal ( LargeFileBase ): def __init__ ( self , name : str , mode : str = \"r\" , * , buffering : int = - 1 , encoding : Optional [ str ] = None , errors : Optional [ str ] = None , newline : Optional [ str ] = None , version : Optional [ int ] = None , keep_last_n : Optional [ int ] = None , ): super () . __init__ ( name , mode , buffering = buffering , encoding = encoding , errors = errors , newline = newline , version = version , keep_last_n = keep_last_n , cache_only_mode = True , ) super () . configure_credentials () def _find_remote_instances ( self ) -> List [ DataInstance ]: return [] def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : raise NotImplementedError () def _upload ( self , local_path : Path , hide_progress : bool ) -> None : pass # the \"upload\" is already done py the parent's caching mechanism def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version (keep_last_n= { self . _keep_last_n } ): { i . remote_path } \" ) i . remote_path . unlink ()","title":"LargeFileLocal"},{"location":"reference/large-file/#great_ai.large_file.LargeFileS3","text":"Bases: LargeFileBase Store large files in S3. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test ') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` Source code in great_ai/large_file/large_file/large_file_s3.py 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 class LargeFileS3 ( LargeFileBase ): \"\"\" Store large files in S3. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test\\n') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` \"\"\" region_name = None access_key_id = None secret_access_key = None bucket_name = None endpoint_url = None @classmethod def configure_credentials ( # type: ignore cls , * , aws_region_name : str , aws_access_key_id : str , aws_secret_access_key : str , large_files_bucket_name : str , aws_endpoint_url : Optional [ str ] = None , ** _ : Any , ) -> None : cls . region_name = aws_region_name cls . access_key_id = aws_access_key_id cls . secret_access_key = aws_secret_access_key cls . bucket_name = large_files_bucket_name cls . endpoint_url = aws_endpoint_url super () . configure_credentials () @cached_property def _client ( self ) -> boto3 . client : if ( self . region_name is None or self . access_key_id is None or self . secret_access_key is None or self . bucket_name is None ): raise ValueError ( \"Please configure the S3 access options by calling LargeFileS3.configure_credentials or set offline_mode=True in the constructor.\" ) return boto3 . client ( \"s3\" , aws_access_key_id = self . access_key_id , aws_secret_access_key = self . secret_access_key , region_name = self . region_name , endpoint_url = self . endpoint_url , ) def _find_remote_instances ( self ) -> List [ DataInstance ]: logger . debug ( f \"Fetching S3 versions of { self . _name } \" ) found_objects = self . _client . list_objects_v2 ( Bucket = self . bucket_name , Prefix = self . _name ) return ( [ DataInstance ( name = o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ 0 ], version = int ( o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = o [ \"Key\" ], ) for o in found_objects [ \"Contents\" ] if o [ \"Key\" ] . split ( S3_NAME_VERSION_SEPARATOR )[ 0 ] == self . _name ] if \"Contents\" in found_objects else [] ) def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Downloading { remote_path } from S3\" ) size = self . _client . head_object ( Bucket = self . bucket_name , Key = remote_path )[ \"ContentLength\" ] self . _client . download_file ( Bucket = self . bucket_name , Key = remote_path , Filename = str ( local_path ), Callback = None if hide_progress else DownloadProgressBar ( name = str ( remote_path ), size = size , logger = logger ), ) def _upload ( self , local_path : Path , hide_progress : bool ) -> None : key = f \" { self . _name } / { self . version } \" logger . info ( f \"Uploading { self . _local_name } to S3 as { key } \" ) self . _client . upload_file ( Filename = str ( local_path ), Bucket = self . bucket_name , Key = key , Callback = None if hide_progress else UploadProgressBar ( path = local_path , logger = logger ), ) def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version from S3 (keep_last_n= { self . _keep_last_n } ): { i . remote_path } \" ) self . _client . delete_object ( Bucket = self . bucket_name , Key = i . remote_path )","title":"LargeFileS3"},{"location":"reference/large-file/#great_ai.large_file.LargeFileMongo","text":"Bases: LargeFileBase Source code in great_ai/large_file/large_file/large_file_mongo.py 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 class LargeFileMongo ( LargeFileBase ): mongo_connection_string = None mongo_database = None @classmethod def configure_credentials ( # type: ignore cls , * , mongo_connection_string : str , mongo_database : str , ** _ : Any , ) -> None : cls . mongo_connection_string = mongo_connection_string cls . mongo_database = mongo_database super () . configure_credentials () @cached_property def _client ( self ) -> GridFSBucket : if self . mongo_connection_string is None or self . mongo_database is None : raise ValueError ( \"Please configure the MongoDB access options by calling LargeFileMongo.configure_credentials or set offline_mode=True in the constructor.\" ) db : Database = MongoClient ( self . mongo_connection_string )[ self . mongo_database ] return GridFSBucket ( db ) def _find_remote_instances ( self ) -> List [ DataInstance ]: logger . debug ( f \"Fetching Mongo (GridFS) versions of { self . _name } \" ) return [ DataInstance ( name = MONGO_NAME_VERSION_SEPARATOR . join ( f . name . split ( MONGO_NAME_VERSION_SEPARATOR )[: - 1 ] ), version = int ( f . name . split ( MONGO_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = ( f . _id , f . length ), origin = \"mongodb\" , ) for f in self . _client . find ( { \"filename\" : re . compile ( re . escape ( self . _name + MONGO_NAME_VERSION_SEPARATOR ) + \".*\" ) } ) ] def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Downloading { remote_path [ 0 ] } from Mongo (GridFS)\" ) progress = ( DownloadProgressBar ( name = str ( remote_path [ 0 ]), size = remote_path [ 1 ], logger = logger ) if not hide_progress else None ) with self . _client . open_download_stream ( remote_path [ 0 ]) as stream : with open ( local_path , \"wb\" ) as f : while True : content = stream . read ( DEFAULT_CHUNK_SIZE ) f . write ( content ) if progress : progress ( len ( content )) if len ( content ) < DEFAULT_CHUNK_SIZE : break def _upload ( self , local_path : Path , hide_progress : bool ) -> None : logger . info ( f \"Uploading { local_path } to Mongo (GridFS)\" ) progress = ( UploadProgressBar ( path = local_path , logger = logger ) if not hide_progress else None ) with self . _client . open_upload_stream ( f \" { self . _name }{ MONGO_NAME_VERSION_SEPARATOR }{ self . version } \" ) as stream : with open ( local_path , \"rb\" ) as f : while True : content = f . read ( DEFAULT_CHUNK_SIZE ) stream . write ( content ) if progress : progress ( len ( content )) if len ( content ) < DEFAULT_CHUNK_SIZE : break def _delete_old_remote_versions ( self ) -> None : if self . _keep_last_n is not None : for i in ( self . _instances [: - self . _keep_last_n ] if self . _keep_last_n > 0 else self . _instances ): logger . info ( f \"Removing old version from MongoDB (GridFS) (keep_last_n= { self . _keep_last_n } ): { i . name }{ MONGO_NAME_VERSION_SEPARATOR }{ i . version } \" ) self . _client . delete ( i . remote_path [ 0 ])","title":"LargeFileMongo"},{"location":"reference/large-file/#great_ai.large_file.LargeFileBase","text":"Bases: ABC Store large files remotely. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test ') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` Source code in great_ai/large_file/large_file/large_file_base.py 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 class LargeFileBase ( ABC ): \"\"\" Store large files remotely. Use local cache for speed up. Examples: ``` with LargeFile(\"test.txt\", \"w\", keep_last_n=3) as f: for i in range(1000000): f.write('test\\n') with LargeFile(\"test.txt\", \"r\") as f: print(f.readlines()[0]) path_to_cached_text_file = LargeFile(\"test.txt\", version=0).get() ``` By default, files are stored in the \".cache\" folder and the least recently use is deleted after the overall size reaches 30 GBs. Change it with the following properties. ``` LargeFile.cache_path = Path(\".cache\") LargeFile.max_cache_size = \"30GB\" ``` \"\"\" initialized = False cache_path = Path ( \".cache\" ) max_cache_size : Optional [ str ] = \"30GB\" def __init__ ( self , name : str , mode : str = \"r\" , * , buffering : int = - 1 , encoding : Optional [ str ] = None , errors : Optional [ str ] = None , newline : Optional [ str ] = None , version : Optional [ int ] = None , keep_last_n : Optional [ int ] = None , cache_only_mode : bool = False , ): self . _name = name self . _version = version self . _mode = mode self . _keep_last_n = keep_last_n self . _cache_only_mode = cache_only_mode self . _buffering = buffering self . _encoding = encoding self . _errors = errors self . _newline = newline LargeFileBase . cache_path . mkdir ( parents = True , exist_ok = True ) self . _find_instances () self . _check_mode_and_set_version () @classmethod def configure_credentials_from_file ( cls , secrets : Union [ Path , str , ConfigFile ], ) -> None : if not isinstance ( secrets , ConfigFile ): secrets = ConfigFile ( secrets ) cls . configure_credentials ( ** { k . lower (): v for k , v in secrets . items ()}) @classmethod def configure_credentials ( cls , ** kwargs : str ) -> None : cls . initialized = True def __enter__ ( self ) -> IO : self . _file : IO [ Any ] = ( tempfile . NamedTemporaryFile ( mode = self . _mode , buffering = self . _buffering , encoding = self . _encoding , newline = self . _newline , errors = self . _errors , delete = False , prefix = \"large_file-\" , ) if \"w\" in self . _mode else open ( self . get (), mode = self . _mode , buffering = self . _buffering , encoding = self . _encoding , newline = self . _newline , errors = self . _errors , ) ) return self . _file def __exit__ ( self , type : Optional [ Type [ BaseException ]], exc : Optional [ BaseException ], traceback : Optional [ TracebackType ], ) -> Literal [ False ]: self . _file . close () if type is None : if \"w\" in self . _mode : self . push ( Path ( self . _file . name )) os . unlink ( self . _file . name ) else : logger . exception ( \"Could not finish operation.\" ) return False @property def _local_name ( self ) -> str : return f \" { self . _name }{ CACHE_NAME_VERSION_SEPARATOR }{ self . version } \" @property def version ( self ) -> int : return cast ( int , self . _version ) @lru_cache ( 1 ) def get ( self , hide_progress : bool = False ) -> Path : remote_path = next ( i . remote_path for i in self . _instances if i . version == self . _version ) destination = self . cache_path / self . _local_name if not destination . exists (): logger . info ( f \"File { self . _local_name } does not exist locally\" ) with tempfile . TemporaryDirectory () as tmp : local_root_path = Path ( tmp ) tmp_file_archive = ( local_root_path / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _download ( remote_path , tmp_file_archive , hide_progress = hide_progress ) logger . info ( f \"Decompressing { self . _local_name } \" ) shutil . unpack_archive ( str ( tmp_file_archive ), tmp , COMPRESSION_ALGORITHM ) shutil . move ( str ( local_root_path / self . _local_name ), str ( destination )) else : logger . info ( f \"File { self . _local_name } found in cache\" ) return destination def push ( self , path : Union [ Path , str ], hide_progress : bool = False ) -> None : if isinstance ( path , str ): path = Path ( path ) with tempfile . TemporaryDirectory () as tmp : if path . is_file (): logger . info ( f \"Copying file for { self . _local_name } \" ) copy : Any = shutil . copy else : logger . info ( f \"Copying directory for { self . _local_name } \" ) copy = shutil . copytree try : # Make local copy in the cache shutil . rmtree ( self . cache_path / self . _local_name , ignore_errors = True ) copy ( str ( path ), str ( self . cache_path / self . _local_name )) except shutil . SameFileError : pass # No worries copy ( str ( path ), str ( Path ( tmp ) / self . _local_name )) with tempfile . TemporaryDirectory () as tmp2 : # A directory has to be zipped and it cannot contain the output of the zipping logger . info ( f \"Compressing { self . _local_name } \" ) shutil . make_archive ( str ( Path ( tmp2 ) / self . _local_name ), COMPRESSION_ALGORITHM , tmp , ) file_to_be_uploaded = ( Path ( tmp2 ) / f \" { self . _local_name }{ ARCHIVE_EXTENSION } \" ) self . _upload ( file_to_be_uploaded , hide_progress = hide_progress ) self . clean_up () def delete ( self ) -> None : self . _keep_last_n = 0 self . _delete_old_remote_versions () def _find_instances ( self ) -> None : if self . _cache_only_mode : self . _instances = self . _find_instances_from_cache () else : self . _instances = self . _find_remote_instances () self . _instances = sorted ( self . _instances , key = lambda i : i . version ) def _find_instances_from_cache ( self ) -> List [ DataInstance ]: logger . info ( f \"Fetching cached versions of { self . _name } \" ) candidates = [ DataInstance ( name = CACHE_NAME_VERSION_SEPARATOR . join ( f . name . split ( CACHE_NAME_VERSION_SEPARATOR )[: - 1 ] ), version = int ( f . name . split ( CACHE_NAME_VERSION_SEPARATOR )[ - 1 ]), remote_path = f , ) for f in self . cache_path . glob ( f \" { self . _name }{ CACHE_NAME_VERSION_SEPARATOR } *\" ) ] return [ c for c in candidates if c . name == self . _name ] def _check_mode_and_set_version ( self ) -> None : if \"+\" in self . _mode : raise ValueError ( f \"File mode ` { self . _mode } ` is not allowed3, remove the `+`.\" ) if \"w\" in self . _mode : if self . _version is not None : raise ValueError ( \"Providing a version is not allowed in write mode.\" ) self . _version = self . _instances [ - 1 ] . version + 1 if self . _instances else 0 elif \"r\" in self . _mode : if not self . _instances : raise FileNotFoundError ( f \"File { self . _name } not found. No versions are available.\" ) if self . _version is None : self . _version = self . _instances [ - 1 ] . version logger . info ( f \"Latest version of { self . _name } is { self . _version } \" + f \"(from versions: { self . versions_pretty } )\" ) elif self . _version not in [ i . version for i in self . _instances ]: raise FileNotFoundError ( f \"File { self . _name } not found with version { self . _version } . \" + f \"(from versions: { self . versions_pretty } )\" ) else : raise ValueError ( \"Unsupported file mode.\" ) @property def versions_pretty ( self ) -> str : return \", \" . join (( str ( i . version ) for i in self . _instances )) def clean_up ( self ) -> None : self . _delete_old_remote_versions () self . _prune_cache () def _prune_cache ( self ) -> None : self . cache_path . mkdir ( parents = True , exist_ok = True ) if self . max_cache_size is None : return allowed_size = human_readable_to_byte ( self . max_cache_size ) assert allowed_size >= 0 least_recently_read = sorted ( [ f for f in self . cache_path . glob ( \"*\" )], key = lambda f : f . stat () . st_atime ) while sum ( os . path . getsize ( f ) for f in least_recently_read ) > allowed_size : file = least_recently_read . pop ( 0 ) logger . info ( f \"Deleting file from cache to meet quota (max_cache_size= { self . max_cache_size } ): { file } \" ) os . unlink ( file ) @abstractmethod def _find_remote_instances ( self ) -> List [ DataInstance ]: pass @abstractmethod def _download ( self , remote_path : Any , local_path : Path , hide_progress : bool ) -> None : pass @abstractmethod def _upload ( self , local_path : Path , hide_progress : bool ) -> None : pass @abstractmethod def _delete_old_remote_versions ( self ) -> None : pass","title":"LargeFileBase"},{"location":"reference/utilities/","text":"Utilities # from great_ai.utilities import * NLP tools # Well-tested tools that can be used in production with confidence. The toolbox of feature-extraction functions is expected to grow to cover other domains as well. clean ( text , ignore_xml = False , ignore_latex = False , remove_brackets = False , convert_to_ascii = False ) # Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean ( '<h2 color=\"red\">Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 &lt;3</h2>' ) 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean ( ... '<h2 color=\"red\">Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 &lt;3</h2>' , ... convert_to_ascii = True ... ) 'Bjorn is happy <3' >>> clean ( ... '<h2 color=\"red\">Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 &lt;3</h2>' , ... ignore_xml = True ... ) '<h2 color=\"red\">Bj\u00f6rn is happy \ud83d\ude42 lt;3</h2>' Parameters: Name Type Description Default text str Text to be cleaned. required ignore_xml bool Do not process/remove XML-tags. False ignore_latex bool Do not process/remove LaTeX-tags. False remove_brackets bool Do not remove brackets ([]) False convert_to_ascii bool Strip (or convert) non-ascii characters. False Returns: Type Description str The cleaned input text with sensibly collapsed whitespace and optionally no markup. Source code in great_ai/utilities/clean.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 def clean ( text : str , ignore_xml : bool = False , ignore_latex : bool = False , remove_brackets : bool = False , convert_to_ascii : bool = False , ) -> str : \"\"\"Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean('<h2 color=\"red\">Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 &lt;3</h2>') 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean( ... '<h2 color=\"red\">Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 &lt;3</h2>', ... convert_to_ascii=True ... ) 'Bjorn is happy <3' >>> clean( ... '<h2 color=\"red\">Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 &lt;3</h2>', ... ignore_xml=True ... ) '<h2 color=\"red\">Bj\u00f6rn is happy \ud83d\ude42 lt;3</h2>' Args: text: Text to be cleaned. ignore_xml: Do not process/remove XML-tags. ignore_latex: Do not process/remove LaTeX-tags. remove_brackets: Do not remove brackets ([]) convert_to_ascii: Strip (or convert) non-ascii characters. Returns: The cleaned input text with sensibly collapsed whitespace and optionally no markup. \"\"\" if not ignore_xml : text = re . sub ( r \"<[^>]*>\" , \" \" , text ) text = html . unescape ( text ) if not ignore_latex : text = text . replace ( \"%\" , \" \\\\ %\" ) # escape LaTeX comments before parsing as LaTeX try : text = latex . latex_to_text ( text , tolerant_parsing = True , strict_braces = False ) text = text . replace ( \" %s \" , \" \" ) except : logger . exception ( \"Latex parsing error\" ) if convert_to_ascii : text = unicodedata . normalize ( \"NFKD\" , text ) try : text . encode ( \"ASCII\" , errors = \"strict\" ) except UnicodeEncodeError : text = \"\" . join ([ c for c in text if not unicodedata . combining ( c )]) text = unidecode . unidecode ( text ) text = re . sub ( r \"\\b[a-zA-Z](?:[\\t ]+[a-zA-Z]\\b)+\" , lambda m : re . sub ( r \"[\\t ]\" , \"\" , m [ 0 ]), text ) # A R T I C L E => ARTICLE if remove_brackets : text = re . sub ( r \"\\[[^\\]]*\\]\" , \" \" , text ) # fix hypens: break- word => break-word text = re . sub ( r \"(\\S)-\\s+\" , r \"\\1-\" , text ) text = re . sub ( r \"\\s+-(\\S)\" , r \"-\\1\" , text ) # collapse whitespace text = re . sub ( r \"\\s+\" , \" \" , text ) # fix punctuation text = re . sub ( rf \" ([ { joined_left_punctuations } ])\" , r \"\\1\" , text ) text = re . sub ( rf \"([ { joined_right_punctuations } ]) \" , r \"\\1\" , text ) text = text . strip () return text get_sentences ( text , ignore_partial = False , true_case = False , remove_punctuation = False ) # Return the list of sentences found in the input text. Use syntok to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences ( 'This is a sentence. This is a half' ) ['This is a sentence.', 'This is a half'] >>> get_sentences ( 'This is a sentence. This is a half' , ignore_partial = True ) ['This is a sentence.'] >>> get_sentences ( 'I like Apple.' , true_case = True , remove_punctuation = True ) ['i like Apple'] Parameters: Name Type Description Default text str Text to be segmented into sentences. required ignore_partial bool Filter out sentences that are not capitalised/don't end with a punctuation. False true_case bool Crude method: lowercase the first word of each sentence. False remove_punctuation bool Remove all kinds of punctuation. False Returns: Type Description List [ str ] The found sentences (with partial sentences optionally filtered out). Source code in great_ai/utilities/get_sentences.py 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 def get_sentences ( text : str , ignore_partial : bool = False , true_case : bool = False , remove_punctuation : bool = False , ) -> List [ str ]: \"\"\"Return the list of sentences found in the input text. Use [syntok](https://github.com/fnl/syntok) to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences('This is a sentence. This is a half') ['This is a sentence.', 'This is a half'] >>> get_sentences('This is a sentence. This is a half', ignore_partial=True) ['This is a sentence.'] >>> get_sentences('I like Apple.', true_case=True, remove_punctuation=True) ['i like Apple'] Args: text: Text to be segmented into sentences. ignore_partial: Filter out sentences that are not capitalised/don't end with a punctuation. true_case: Crude method: lowercase the first word of each sentence. remove_punctuation: Remove all kinds of punctuation. Returns: The found sentences (with partial sentences optionally filtered out). \"\"\" tokenizer = Tokenizer ( emit_hyphen_or_underscore_sep = True , replace_not_contraction = False ) token_stream = tokenizer . tokenize ( text ) def process ( sentence : str ) -> str : if true_case : sentence = sentence [ 0 ] . lower () + sentence [ 1 :] # very crude method if remove_punctuation : sentence = re . sub ( punctuations_pattern , \" \" , sentence ) return sentence . strip () sentences = [ process ( tokenizer . to_text ( sentence )) for sentence in segment ( token_stream ) ] if ignore_partial : sentences = [ sentence for sentence in sentences if sentence [ 0 ] . isupper () and sentence [ - 1 ] in sentence_ending_punctuations ] return sentences predict_language ( text ) # Predict the language code from text. A thin wrapper over langcodes for convenient language tagging. Examples: >>> predict_language ( 'This is a sentence.' ) 'en' Parameters: Name Type Description Default text Optional [ str ] Text used for prediction. required Returns: Type Description str The predicted language code (en, en-US) or und if a prediction could not be made. Source code in great_ai/utilities/language/predict_language.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def predict_language ( text : Optional [ str ]) -> str : \"\"\"Predict the language code from text. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> predict_language('This is a sentence.') 'en' Args: text: Text used for prediction. Returns: The predicted language code (en, en-US) or `und` if a prediction could not be made. \"\"\" if not text : return Language . make () . to_tag () try : language_code = detect ( text ) except LangDetectException : return Language . make () . to_tag () return Language . get ( language_code ) . to_tag () english_name_of_language ( language_code ) # Human-friendly English name of language from its language_code . A thin wrapper over langcodes for convenient language tagging. Examples: >>> english_name_of_language ( 'en-US' ) 'English (United States)' >>> english_name_of_language ( 'und' ) 'Unknown language' Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by great_ai.utilities.language.predict_language.predict_language . required Returns: Type Description str English name of language. Source code in great_ai/utilities/language/english_name_of_language.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 def english_name_of_language ( language_code : Optional [ str ]) -> str : \"\"\"Human-friendly English name of language from its `language_code`. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> english_name_of_language('en-US') 'English (United States)' >>> english_name_of_language('und') 'Unknown language' Args: language_code: Language code, for example, returned by [great_ai.utilities.language.predict_language.predict_language][]. Returns: English name of language. \"\"\" if not language_code : language_code = \"und\" return Language . get ( language_code ) . display_name () is_english ( language_code ) # Decide whether the language_code is of an English language. A thin wrapper over langcodes for convenient language tagging. Examples: >>> is_english ( 'en-US' ) True >>> is_english ( None ) False >>> is_english ( 'und' ) False Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by ` great_ai.utilities.language.predict_language.predict_language . required Returns: Type Description bool Boolean indicating whether it's English. Source code in great_ai/utilities/language/is_english.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def is_english ( language_code : Optional [ str ]) -> bool : \"\"\"Decide whether the `language_code` is of an English language. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> is_english('en-US') True >>> is_english(None) False >>> is_english('und') False Args: language_code: Language code, for example, returned by `[great_ai.utilities.language.predict_language.predict_language][]. Returns: Boolean indicating whether it's English. \"\"\" if not language_code : language_code = \"und\" language_code = standardize_tag ( language_code ) return tag_distance ( language_code , \"en\" ) < 15 evaluate_ranking ( expected , actual_scores , target_recall , title = '' , disable_interpolation = False , axes = None , output_svg = None , reverse_order = False , plot = True ) # Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's PR-curve Parameters: Name Type Description Default expected List [ T ] Expected ordering of the elements (rank if it's an integer, alphabetical if a string) required actual_scores List [ float ] Actual ranking scores (need not be on the same scale as expected ) required title Optional [ str ] Title of the plot. '' disable_interpolation bool Do not interpolate. False axes Optional [ plt . Axes ] Matplotlib axes for ploting inside a subplot. None output_svg Optional [ Path ] If specified, save the chart as an svg to the given Path. None reverse_order bool Reverse the ranking specified by expected . False plot bool Display a plot on the screen. True Returns: Type Description Dict [ T , float ] Precision values at given recall. Source code in great_ai/utilities/evaluate_ranking/evaluate_ranking.py 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 def evaluate_ranking ( expected : List [ T ], actual_scores : List [ float ], target_recall : float , title : Optional [ str ] = \"\" , disable_interpolation : bool = False , axes : Optional [ plt . Axes ] = None , output_svg : Optional [ Path ] = None , reverse_order : bool = False , plot : bool = True , ) -> Dict [ T , float ]: \"\"\"Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's [PR-curve](https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py) Args: expected: Expected ordering of the elements (rank if it's an integer, alphabetical if a string) actual_scores: Actual ranking scores (need not be on the same scale as `expected`) title: Title of the plot. disable_interpolation: Do not interpolate. axes: Matplotlib axes for ploting inside a subplot. output_svg: If specified, save the chart as an svg to the given Path. reverse_order: Reverse the ranking specified by `expected`. plot: Display a plot on the screen. Returns: Precision values at given recall. \"\"\" assert 0 <= target_recall <= 1 if plot and axes is None : fig = plt . figure ( figsize = ( 20 , 10 )) fig . patch . set_facecolor ( \"white\" ) ax = plt . axes () else : ax = axes classes = sorted ( unique ( expected ), reverse = reverse_order ) str_classes = [ str ( c ) for c in classes ] with matplotlib . rc_context ({ \"font.size\" : 20 }): if plot : ax . set_xmargin ( 0 ) draw_f1_iso_lines ( axes = ax ) results : Dict [ T , float ] = {} for i in range ( len ( classes ) - 1 ): binarized_expected = [ ( v < classes [ i ]) if reverse_order else ( v > classes [ i ]) for v in expected ] precision , recall , _ = precision_recall_curve ( binarized_expected , actual_scores ) if not disable_interpolation : for j in range ( 1 , len ( precision )): precision [ j ] = max ( precision [ j - 1 ], precision [ j ]) closest_recall_index = np . argmin ( np . abs ( recall - target_recall )) precision_at_closest_recall = precision [ closest_recall_index ] average_precision = average_precision_score ( binarized_expected , actual_scores ) results [ classes [ i ]] = precision_at_closest_recall if plot : ax . plot ( recall , precision , label = f \" { '|' . join ( str_classes [: i + 1 ]) } \u2194 { '|' . join ( str_classes [ i + 1 :]) } (P@ { target_recall : .2f } = { precision_at_closest_recall : .2f } , AP= { average_precision : .2f } )\" , ) if plot : ax . legend ( loc = \"upper right\" ) ax . axvline ( x = target_recall , linestyle = \"--\" , color = \"#55c6bb\" , linewidth = 2.0 ) if title is None : title = \"Ranking evaluation\" ax . set_title ( f ' { title } ( { \" < \" . join ( str_classes ) } )' , pad = 20 ) ax . set_xlabel ( \"Recall\" ) ax . set_ylabel ( \"Precision\" ) ax . set_xticks ([ target_recall ] + sorted ( ax . get_xticks ())) if plot and output_svg is None : if axes is None : plt . show () elif output_svg : plt . savefig ( output_svg , format = \"svg\" ) return results Parallel processing # Multiprocessing and multithreading-based parallelism with support for async functions. Its main purpose is to implement great_ai.GreatAI.process_batch , however, the parallel processing functions are also convenient for covering other types of mapping needs with a friendlier API than joblib or multiprocess . simple_parallel_map ( func , input_values , * , chunk_size = None , concurrency = None ) # Execute a map operation on an list mimicking the API of the built-in map() . A thin-wrapper over parallel_map . For more options, consult the documentation of parallel_map . Examples: >>> import math >>> list ( simple_parallel_map ( math . sqrt , [ 9 , 4 , 1 ])) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Sequence [ T ] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None Returns: Type Description List [ V ] An iterable of results obtained from applying func to each input value. Raises: Type Description WorkerException If there was an error in the func function in a background process. Source code in great_ai/utilities/parallel_map/simple_parallel_map.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 def simple_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Sequence [ T ], * , chunk_size : Optional [ int ] = None , concurrency : Optional [ int ] = None , ) -> List [ V ]: \"\"\"Execute a map operation on an list mimicking the API of the built-in `map()`. A thin-wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. For more options, consult the documentation of [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. Examples: >>> import math >>> list(simple_parallel_map(math.sqrt, [9, 4, 1])) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. Returns: An iterable of results obtained from applying `func` to each input value. Raises: WorkerException: If there was an error in the `func` function in a background process. \"\"\" input_values = list ( input_values ) # in case the input is mistakenly not a sequence generator = parallel_map ( func = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) return list ( tqdm ( generator , total = len ( input_values ), ) ) parallel_map ( func , input_values , * , chunk_size = None , ignore_exceptions = False , concurrency = None , unordered = False ) # Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and async map functions. The func function is serialised with dill . Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some startup cost. Each process processes a single chunk at once. Examples: >>> import math >>> list ( parallel_map ( math . sqrt , [ 9 , 4 , 1 ], concurrency = 2 )) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background process and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/parallel_map.py 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 def parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and `async` map functions. The `func` function is serialised with `dill`. Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some startup cost. Each process processes a single chunk at once. Examples: >>> import math >>> list(parallel_map(math.sqrt, [9, 4, 1], concurrency=2)) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background process and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) ctx = ( mp . get_context ( \"fork\" ) if \"fork\" in mp . get_all_start_methods () else mp . get_context ( \"spawn\" ) ) ctx . freeze_support () manager = ctx . Manager () input_queue = manager . Queue ( config . concurrency * 2 ) output_queue = manager . Queue ( config . concurrency * 2 ) should_stop = ctx . Event () serialized_map_function = dill . dumps ( func , byref = True , recurse = False ) processes = [ ctx . Process ( name = f \"parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = serialized_map_function , ), ) for i in range ( config . concurrency ) ] for p in processes : p . start () try : yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () except WorkerException : should_stop . set () raise except Exception : for p in processes : p . terminate () p . kill () raise finally : for p in processes : p . join () # terminated processes have to be joined else they remain zombies p . close () manager . shutdown () threaded_parallel_map ( func , input_values , * , chunk_size = None , ignore_exceptions = False , concurrency = None , unordered = False ) # Execute a map operation on an iterable stream. Similar to parallel_map but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and async map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list ( threaded_parallel_map ( lambda x : x ** 2 , [ 1 , 2 , 3 ])) [1, 4, 9] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new threads to start. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background thread and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/threaded_parallel_map.py 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 def threaded_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. Similar to [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and `async` map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list(threaded_parallel_map(lambda x: x ** 2, [1, 2, 3])) [1, 4, 9] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new threads to start. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background thread and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) input_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) output_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) should_stop = threading . Event () threads = [ threading . Thread ( name = f \"threaded_parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = func , ), ) for i in range ( config . concurrency ) ] for t in threads : t . start () yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () for t in threads : t . join ( 1 ) Composable parallel processing # Because both threaded_parallel_map and parallel_map have a streaming interface, it is easy to compose them and end up with, for example, a process for each CPU core with its own thread-pool or event-loop. Longer pipelines are also easy to imagine. The chunking methods help in these compositions. chunk ( values , chunk_size ) # Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length chunk_size except the last one the length of which is between 1 and chunk_size . Useful for parallel processing. Examples: >>> list ( chunk ( range ( 10 ), chunk_size = 3 )) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Parameters: Name Type Description Default values Iterable [ T ] The stream of items to pack into chunks. required chunk_size int Desired length of each (but the last) chunk. required Yields: Type Description Iterable [ List [ T ]] The next chunk. Source code in great_ai/utilities/chunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def chunk ( values : Iterable [ T ], chunk_size : int ) -> Iterable [ List [ T ]]: \"\"\"Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length `chunk_size` except the last one the length of which is between 1 and `chunk_size`. Useful for parallel processing. Examples: >>> list(chunk(range(10), chunk_size=3)) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Args: values: The stream of items to pack into chunks. chunk_size: Desired length of each (but the last) chunk. Yields: The next chunk. \"\"\" assert chunk_size >= 1 result : List [ T ] = [] for v in values : result . append ( v ) if len ( result ) == chunk_size : yield result result = [] if len ( result ) > 0 : yield result unchunk ( chunks ) # Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of chunk . Useful for parallel processing. Similar to itertools.chain but ignores None chunks. Examples: >>> list ( unchunk ([[ 0 , 1 , 2 ], [ 3 , 4 , 5 ], [ 6 , 7 , 8 ], [ 9 ]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Parameters: Name Type Description Default chunks Iterable [ Optional [ Iterable [ T ]]] Stream of chunks to unpack. required Yields: Type Description Iterable [ T ] The next item in the flattened iterable. Source code in great_ai/utilities/unchunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 def unchunk ( chunks : Iterable [ Optional [ Iterable [ T ]]]) -> Iterable [ T ]: \"\"\"Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of [chunk][great_ai.utilities.chunk.chunk]. Useful for parallel processing. Similar to itertools.chain but ignores `None` chunks. Examples: >>> list(unchunk([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Args: chunks: Stream of chunks to unpack. Yields: The next item in the flattened iterable. \"\"\" for chunk in chunks : if chunk is not None : yield from chunk Operations # ConfigFile # Bases: Mapping [ str , str ] A small and safe INI -style configuration loader with dict and ENV support. The values can be accessed using both dot- and index-notation. It is compatible with the dict interface. File format example: # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV : MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. Examples: >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . zeroth_key 'test' >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . second_key Traceback (most recent call last): ... KeyError : 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile ( 'tests/utilities/data/simple.conf' ) >>> { ** a } {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} Source code in great_ai/utilities/config_file/config_file.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 class ConfigFile ( Mapping [ str , str ]): \"\"\"A small and safe `INI`-style configuration loader with `dict` and `ENV` support. The values can be accessed using both dot- and index-notation. It is compatible with the `dict` interface. File format example: ```toml # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV:MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. ``` Examples: >>> ConfigFile('tests/utilities/data/simple.conf') ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile('tests/utilities/data/simple.conf').zeroth_key 'test' >>> ConfigFile('tests/utilities/data/simple.conf').second_key Traceback (most recent call last): ... KeyError: 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile('tests/utilities/data/simple.conf') >>> {**a} {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} \"\"\" ENVIRONMENT_VARIABLE_KEY_PREFIX = \"ENV\" def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse () @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path def _parse ( self ) -> None : with open ( self . _path , encoding = \"utf-8\" ) as f : lines : str = f . read () matches = pattern . findall ( lines ) for key , * values in matches : try : value = next ( v for v in values if v ) except StopIteration : raise ParseError ( f \"\"\"Cannot parse config file ( { self . _path . absolute () } ), error at key ` { key } `\"\"\" ) already_exists = key in self . _key_values if already_exists and not value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): raise KeyError ( f \"Key ` { key } ` has been already defined and its value is ` { self . _key_values [ key ] } `\" ) if value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): _ , value = value . split ( \":\" ) if value not in os . environ : issue = f \"\"\"The value of ` { key } ` contains the \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } ` prefix but ` { value } ` is not defined as an environment variable\"\"\" if already_exists : logger . warning ( f \"\"\" { issue } , using the default value defined above (` { self . _key_values [ key ] } `)\"\"\" ) continue elif self . _ignore_missing : logger . warning ( issue ) else : raise ValueError ( f \" { issue } and no default value has been provided\" ) else : value = os . environ [ value ] self . _key_values [ key ] = value def __getattr__ ( self , key : str ) -> str : if key in self . _key_values : return self . _key_values [ key ] raise KeyError ( f \"Key ` { key } ` is not found in configuration file ( { self . _path . absolute () } )\" ) __getitem__ = __getattr__ def __iter__ ( self ) -> Iterator [ str ]: return iter ( self . _key_values ) def __len__ ( self ) -> int : return len ( self . _key_values ) def keys ( self ) -> KeysView [ str ]: return self . _key_values . keys () def values ( self ) -> ValuesView [ str ]: return self . _key_values . values () def items ( self ) -> ItemsView [ str , str ]: return self . _key_values . items () def __repr__ ( self ) -> str : return f \" { type ( self ) . __name__ } (path= { self . _path . as_posix () } ) { self . _key_values } \" __init__ ( path , * , ignore_missing = False ) # Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Parameters: Name Type Description Default path Union [ Path , str ] Local path of the configuration file. required ignore_missing bool Don't raise an exception on missing environment variables. False Raises: Type Description FileNotFoundError If there is no file at the specified path. ParseError If the provided file does not conform to the expected format. KeyError If there is duplication in the keys. ValueError If an environment variable is referenced but it is not set in the system and ignore_missing=False . Source code in great_ai/utilities/config_file/config_file.py 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse () path () property # Original path from where the configuration was loaded. Source code in great_ai/utilities/config_file/config_file.py 83 84 85 86 @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path get_logger ( name , level = logging . INFO , disable_colors = False ) # Return a customised logger used throughout the GreatAI codebase. Uses colors, and only prints timestamps when not running inside notebook. Source code in great_ai/utilities/logger/get_logger.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def get_logger ( name : str , level : int = logging . INFO , disable_colors : bool = False ) -> logging . Logger : \"\"\"Return a customised logger used throughout the GreatAI codebase. Uses colors, and only prints timestamps when not running inside notebook. \"\"\" if name not in loggers : logger = logging . getLogger ( name ) logger . setLevel ( level ) try : get_ipython () # type: ignore log_format = \" %(message)s \" except NameError : # will fail outside of a notebook https://ipython.org/ log_format = \" %(asctime)s | %(levelname)8s | %(message)s \" stdout_handler = logging . StreamHandler () stdout_handler . setLevel ( level ) if not disable_colors : stdout_handler . setFormatter ( CustomFormatter ( log_format )) logger . addHandler ( stdout_handler ) loggers [ name ] = logger return loggers [ name ]","title":"Utilities"},{"location":"reference/utilities/#utilities","text":"from great_ai.utilities import *","title":"Utilities"},{"location":"reference/utilities/#nlp-tools","text":"Well-tested tools that can be used in production with confidence. The toolbox of feature-extraction functions is expected to grow to cover other domains as well.","title":"NLP tools"},{"location":"reference/utilities/#great_ai.utilities.clean.clean","text":"Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean ( '<h2 color=\"red\">Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 &lt;3</h2>' ) 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean ( ... '<h2 color=\"red\">Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 &lt;3</h2>' , ... convert_to_ascii = True ... ) 'Bjorn is happy <3' >>> clean ( ... '<h2 color=\"red\">Bj \\\\ \" {o} rn is \\t \\\\ textit {happy} \ud83d\ude42 &lt;3</h2>' , ... ignore_xml = True ... ) '<h2 color=\"red\">Bj\u00f6rn is happy \ud83d\ude42 lt;3</h2>' Parameters: Name Type Description Default text str Text to be cleaned. required ignore_xml bool Do not process/remove XML-tags. False ignore_latex bool Do not process/remove LaTeX-tags. False remove_brackets bool Do not remove brackets ([]) False convert_to_ascii bool Strip (or convert) non-ascii characters. False Returns: Type Description str The cleaned input text with sensibly collapsed whitespace and optionally no markup. Source code in great_ai/utilities/clean.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 def clean ( text : str , ignore_xml : bool = False , ignore_latex : bool = False , remove_brackets : bool = False , convert_to_ascii : bool = False , ) -> str : \"\"\"Clean all XML, LaTeX, PDF-extraction, and Unicode artifacts from the text. The cleaning is quite heavy-weight and can be destructive. However, when working with text, this is usually required to achieve sufficient cleanliness before further processing. Optionally, the text can be turned into ASCII. Carefully consider whether this is absolutely needed for your use-case. Examples: >>> clean('<h2 color=\"red\">Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 &lt;3</h2>') 'Bj\u00f6rn is happy \ud83d\ude42 <3' >>> clean( ... '<h2 color=\"red\">Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 &lt;3</h2>', ... convert_to_ascii=True ... ) 'Bjorn is happy <3' >>> clean( ... '<h2 color=\"red\">Bj\\\\\\\\\"{o}rn is \\\\t \\\\\\\\textit{happy} \ud83d\ude42 &lt;3</h2>', ... ignore_xml=True ... ) '<h2 color=\"red\">Bj\u00f6rn is happy \ud83d\ude42 lt;3</h2>' Args: text: Text to be cleaned. ignore_xml: Do not process/remove XML-tags. ignore_latex: Do not process/remove LaTeX-tags. remove_brackets: Do not remove brackets ([]) convert_to_ascii: Strip (or convert) non-ascii characters. Returns: The cleaned input text with sensibly collapsed whitespace and optionally no markup. \"\"\" if not ignore_xml : text = re . sub ( r \"<[^>]*>\" , \" \" , text ) text = html . unescape ( text ) if not ignore_latex : text = text . replace ( \"%\" , \" \\\\ %\" ) # escape LaTeX comments before parsing as LaTeX try : text = latex . latex_to_text ( text , tolerant_parsing = True , strict_braces = False ) text = text . replace ( \" %s \" , \" \" ) except : logger . exception ( \"Latex parsing error\" ) if convert_to_ascii : text = unicodedata . normalize ( \"NFKD\" , text ) try : text . encode ( \"ASCII\" , errors = \"strict\" ) except UnicodeEncodeError : text = \"\" . join ([ c for c in text if not unicodedata . combining ( c )]) text = unidecode . unidecode ( text ) text = re . sub ( r \"\\b[a-zA-Z](?:[\\t ]+[a-zA-Z]\\b)+\" , lambda m : re . sub ( r \"[\\t ]\" , \"\" , m [ 0 ]), text ) # A R T I C L E => ARTICLE if remove_brackets : text = re . sub ( r \"\\[[^\\]]*\\]\" , \" \" , text ) # fix hypens: break- word => break-word text = re . sub ( r \"(\\S)-\\s+\" , r \"\\1-\" , text ) text = re . sub ( r \"\\s+-(\\S)\" , r \"-\\1\" , text ) # collapse whitespace text = re . sub ( r \"\\s+\" , \" \" , text ) # fix punctuation text = re . sub ( rf \" ([ { joined_left_punctuations } ])\" , r \"\\1\" , text ) text = re . sub ( rf \"([ { joined_right_punctuations } ]) \" , r \"\\1\" , text ) text = text . strip () return text","title":"clean()"},{"location":"reference/utilities/#great_ai.utilities.get_sentences.get_sentences","text":"Return the list of sentences found in the input text. Use syntok to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences ( 'This is a sentence. This is a half' ) ['This is a sentence.', 'This is a half'] >>> get_sentences ( 'This is a sentence. This is a half' , ignore_partial = True ) ['This is a sentence.'] >>> get_sentences ( 'I like Apple.' , true_case = True , remove_punctuation = True ) ['i like Apple'] Parameters: Name Type Description Default text str Text to be segmented into sentences. required ignore_partial bool Filter out sentences that are not capitalised/don't end with a punctuation. False true_case bool Crude method: lowercase the first word of each sentence. False remove_punctuation bool Remove all kinds of punctuation. False Returns: Type Description List [ str ] The found sentences (with partial sentences optionally filtered out). Source code in great_ai/utilities/get_sentences.py 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 def get_sentences ( text : str , ignore_partial : bool = False , true_case : bool = False , remove_punctuation : bool = False , ) -> List [ str ]: \"\"\"Return the list of sentences found in the input text. Use [syntok](https://github.com/fnl/syntok) to segment the sentences. Further processing can be enabled with optional arguments. Examples: >>> get_sentences('This is a sentence. This is a half') ['This is a sentence.', 'This is a half'] >>> get_sentences('This is a sentence. This is a half', ignore_partial=True) ['This is a sentence.'] >>> get_sentences('I like Apple.', true_case=True, remove_punctuation=True) ['i like Apple'] Args: text: Text to be segmented into sentences. ignore_partial: Filter out sentences that are not capitalised/don't end with a punctuation. true_case: Crude method: lowercase the first word of each sentence. remove_punctuation: Remove all kinds of punctuation. Returns: The found sentences (with partial sentences optionally filtered out). \"\"\" tokenizer = Tokenizer ( emit_hyphen_or_underscore_sep = True , replace_not_contraction = False ) token_stream = tokenizer . tokenize ( text ) def process ( sentence : str ) -> str : if true_case : sentence = sentence [ 0 ] . lower () + sentence [ 1 :] # very crude method if remove_punctuation : sentence = re . sub ( punctuations_pattern , \" \" , sentence ) return sentence . strip () sentences = [ process ( tokenizer . to_text ( sentence )) for sentence in segment ( token_stream ) ] if ignore_partial : sentences = [ sentence for sentence in sentences if sentence [ 0 ] . isupper () and sentence [ - 1 ] in sentence_ending_punctuations ] return sentences","title":"get_sentences()"},{"location":"reference/utilities/#great_ai.utilities.language.predict_language.predict_language","text":"Predict the language code from text. A thin wrapper over langcodes for convenient language tagging. Examples: >>> predict_language ( 'This is a sentence.' ) 'en' Parameters: Name Type Description Default text Optional [ str ] Text used for prediction. required Returns: Type Description str The predicted language code (en, en-US) or und if a prediction could not be made. Source code in great_ai/utilities/language/predict_language.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def predict_language ( text : Optional [ str ]) -> str : \"\"\"Predict the language code from text. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> predict_language('This is a sentence.') 'en' Args: text: Text used for prediction. Returns: The predicted language code (en, en-US) or `und` if a prediction could not be made. \"\"\" if not text : return Language . make () . to_tag () try : language_code = detect ( text ) except LangDetectException : return Language . make () . to_tag () return Language . get ( language_code ) . to_tag ()","title":"predict_language()"},{"location":"reference/utilities/#great_ai.utilities.language.english_name_of_language.english_name_of_language","text":"Human-friendly English name of language from its language_code . A thin wrapper over langcodes for convenient language tagging. Examples: >>> english_name_of_language ( 'en-US' ) 'English (United States)' >>> english_name_of_language ( 'und' ) 'Unknown language' Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by great_ai.utilities.language.predict_language.predict_language . required Returns: Type Description str English name of language. Source code in great_ai/utilities/language/english_name_of_language.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 def english_name_of_language ( language_code : Optional [ str ]) -> str : \"\"\"Human-friendly English name of language from its `language_code`. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> english_name_of_language('en-US') 'English (United States)' >>> english_name_of_language('und') 'Unknown language' Args: language_code: Language code, for example, returned by [great_ai.utilities.language.predict_language.predict_language][]. Returns: English name of language. \"\"\" if not language_code : language_code = \"und\" return Language . get ( language_code ) . display_name ()","title":"english_name_of_language()"},{"location":"reference/utilities/#great_ai.utilities.language.is_english.is_english","text":"Decide whether the language_code is of an English language. A thin wrapper over langcodes for convenient language tagging. Examples: >>> is_english ( 'en-US' ) True >>> is_english ( None ) False >>> is_english ( 'und' ) False Parameters: Name Type Description Default language_code Optional [ str ] Language code, for example, returned by ` great_ai.utilities.language.predict_language.predict_language . required Returns: Type Description bool Boolean indicating whether it's English. Source code in great_ai/utilities/language/is_english.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def is_english ( language_code : Optional [ str ]) -> bool : \"\"\"Decide whether the `language_code` is of an English language. A thin wrapper over [langcodes](https://github.com/rspeer/langcodes) for convenient language tagging. Examples: >>> is_english('en-US') True >>> is_english(None) False >>> is_english('und') False Args: language_code: Language code, for example, returned by `[great_ai.utilities.language.predict_language.predict_language][]. Returns: Boolean indicating whether it's English. \"\"\" if not language_code : language_code = \"und\" language_code = standardize_tag ( language_code ) return tag_distance ( language_code , \"en\" ) < 15","title":"is_english()"},{"location":"reference/utilities/#great_ai.utilities.evaluate_ranking.evaluate_ranking.evaluate_ranking","text":"Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's PR-curve Parameters: Name Type Description Default expected List [ T ] Expected ordering of the elements (rank if it's an integer, alphabetical if a string) required actual_scores List [ float ] Actual ranking scores (need not be on the same scale as expected ) required title Optional [ str ] Title of the plot. '' disable_interpolation bool Do not interpolate. False axes Optional [ plt . Axes ] Matplotlib axes for ploting inside a subplot. None output_svg Optional [ Path ] If specified, save the chart as an svg to the given Path. None reverse_order bool Reverse the ranking specified by expected . False plot bool Display a plot on the screen. True Returns: Type Description Dict [ T , float ] Precision values at given recall. Source code in great_ai/utilities/evaluate_ranking/evaluate_ranking.py 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 def evaluate_ranking ( expected : List [ T ], actual_scores : List [ float ], target_recall : float , title : Optional [ str ] = \"\" , disable_interpolation : bool = False , axes : Optional [ plt . Axes ] = None , output_svg : Optional [ Path ] = None , reverse_order : bool = False , plot : bool = True , ) -> Dict [ T , float ]: \"\"\"Render the Precision-Recall curve of a ranking. And improved version of scikit-learn's [PR-curve](https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py) Args: expected: Expected ordering of the elements (rank if it's an integer, alphabetical if a string) actual_scores: Actual ranking scores (need not be on the same scale as `expected`) title: Title of the plot. disable_interpolation: Do not interpolate. axes: Matplotlib axes for ploting inside a subplot. output_svg: If specified, save the chart as an svg to the given Path. reverse_order: Reverse the ranking specified by `expected`. plot: Display a plot on the screen. Returns: Precision values at given recall. \"\"\" assert 0 <= target_recall <= 1 if plot and axes is None : fig = plt . figure ( figsize = ( 20 , 10 )) fig . patch . set_facecolor ( \"white\" ) ax = plt . axes () else : ax = axes classes = sorted ( unique ( expected ), reverse = reverse_order ) str_classes = [ str ( c ) for c in classes ] with matplotlib . rc_context ({ \"font.size\" : 20 }): if plot : ax . set_xmargin ( 0 ) draw_f1_iso_lines ( axes = ax ) results : Dict [ T , float ] = {} for i in range ( len ( classes ) - 1 ): binarized_expected = [ ( v < classes [ i ]) if reverse_order else ( v > classes [ i ]) for v in expected ] precision , recall , _ = precision_recall_curve ( binarized_expected , actual_scores ) if not disable_interpolation : for j in range ( 1 , len ( precision )): precision [ j ] = max ( precision [ j - 1 ], precision [ j ]) closest_recall_index = np . argmin ( np . abs ( recall - target_recall )) precision_at_closest_recall = precision [ closest_recall_index ] average_precision = average_precision_score ( binarized_expected , actual_scores ) results [ classes [ i ]] = precision_at_closest_recall if plot : ax . plot ( recall , precision , label = f \" { '|' . join ( str_classes [: i + 1 ]) } \u2194 { '|' . join ( str_classes [ i + 1 :]) } (P@ { target_recall : .2f } = { precision_at_closest_recall : .2f } , AP= { average_precision : .2f } )\" , ) if plot : ax . legend ( loc = \"upper right\" ) ax . axvline ( x = target_recall , linestyle = \"--\" , color = \"#55c6bb\" , linewidth = 2.0 ) if title is None : title = \"Ranking evaluation\" ax . set_title ( f ' { title } ( { \" < \" . join ( str_classes ) } )' , pad = 20 ) ax . set_xlabel ( \"Recall\" ) ax . set_ylabel ( \"Precision\" ) ax . set_xticks ([ target_recall ] + sorted ( ax . get_xticks ())) if plot and output_svg is None : if axes is None : plt . show () elif output_svg : plt . savefig ( output_svg , format = \"svg\" ) return results","title":"evaluate_ranking()"},{"location":"reference/utilities/#parallel-processing","text":"Multiprocessing and multithreading-based parallelism with support for async functions. Its main purpose is to implement great_ai.GreatAI.process_batch , however, the parallel processing functions are also convenient for covering other types of mapping needs with a friendlier API than joblib or multiprocess .","title":"Parallel processing"},{"location":"reference/utilities/#great_ai.utilities.simple_parallel_map","text":"Execute a map operation on an list mimicking the API of the built-in map() . A thin-wrapper over parallel_map . For more options, consult the documentation of parallel_map . Examples: >>> import math >>> list ( simple_parallel_map ( math . sqrt , [ 9 , 4 , 1 ])) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Sequence [ T ] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None Returns: Type Description List [ V ] An iterable of results obtained from applying func to each input value. Raises: Type Description WorkerException If there was an error in the func function in a background process. Source code in great_ai/utilities/parallel_map/simple_parallel_map.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 def simple_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Sequence [ T ], * , chunk_size : Optional [ int ] = None , concurrency : Optional [ int ] = None , ) -> List [ V ]: \"\"\"Execute a map operation on an list mimicking the API of the built-in `map()`. A thin-wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. For more options, consult the documentation of [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]. Examples: >>> import math >>> list(simple_parallel_map(math.sqrt, [9, 4, 1])) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. Returns: An iterable of results obtained from applying `func` to each input value. Raises: WorkerException: If there was an error in the `func` function in a background process. \"\"\" input_values = list ( input_values ) # in case the input is mistakenly not a sequence generator = parallel_map ( func = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) return list ( tqdm ( generator , total = len ( input_values ), ) )","title":"simple_parallel_map()"},{"location":"reference/utilities/#great_ai.utilities.parallel_map.parallel_map.parallel_map","text":"Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and async map functions. The func function is serialised with dill . Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some startup cost. Each process processes a single chunk at once. Examples: >>> import math >>> list ( parallel_map ( math . sqrt , [ 9 , 4 , 1 ], concurrency = 2 )) [3.0, 2.0, 1.0] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new processes to start. Shouldn't be too much more than the number of physical cores. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background process and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/parallel_map.py 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 def parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. A custom parallel map operation supporting both synchronous and `async` map functions. The `func` function is serialised with `dill`. Exceptions encountered in the map function are sent to the host process where they are either raised (default) or ignored. The new processes are forked if the OS allows it, otherwise, new Python processes are bootstrapped which can incur some startup cost. Each process processes a single chunk at once. Examples: >>> import math >>> list(parallel_map(math.sqrt, [9, 4, 1], concurrency=2)) [3.0, 2.0, 1.0] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new processes to start. Shouldn't be too much more than the number of physical cores. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background process and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) ctx = ( mp . get_context ( \"fork\" ) if \"fork\" in mp . get_all_start_methods () else mp . get_context ( \"spawn\" ) ) ctx . freeze_support () manager = ctx . Manager () input_queue = manager . Queue ( config . concurrency * 2 ) output_queue = manager . Queue ( config . concurrency * 2 ) should_stop = ctx . Event () serialized_map_function = dill . dumps ( func , byref = True , recurse = False ) processes = [ ctx . Process ( name = f \"parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = serialized_map_function , ), ) for i in range ( config . concurrency ) ] for p in processes : p . start () try : yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () except WorkerException : should_stop . set () raise except Exception : for p in processes : p . terminate () p . kill () raise finally : for p in processes : p . join () # terminated processes have to be joined else they remain zombies p . close () manager . shutdown ()","title":"parallel_map()"},{"location":"reference/utilities/#great_ai.utilities.threaded_parallel_map","text":"Execute a map operation on an iterable stream. Similar to parallel_map but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and async map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list ( threaded_parallel_map ( lambda x : x ** 2 , [ 1 , 2 , 3 ])) [1, 4, 9] Parameters: Name Type Description Default func Callable [[ T ], Union [ V , Awaitable [ V ]]] The function that should be applied to each element of input_values . It can async , in that case, a new event loop is started for each chunk. required input_values Union [ Iterable [ T ], Sequence [ T ]] An iterable of items that func is applied to. required chunk_size Optional [ int ] Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If chunk_size has a __len__ property, the chunk_size is calculated automatically if not given. None ignore_exceptions bool Ignore chunks if next() raises an exception on input_values . And return None if func raised an exception in a worker process. False concurrency Optional [ int ] Number of new threads to start. None unordered bool Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. False Yields: Type Description Iterable [ Optional [ V ]] The next result obtained from applying func to each input value. May contain None -s if ignore_exceptions=True . May have different order than the input if unordered=True . Raises: Type Description WorkerException If there was an error in the func function in a background thread and ignore_exceptions=False . Source code in great_ai/utilities/parallel_map/threaded_parallel_map.py 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 def threaded_parallel_map ( func : Callable [[ T ], Union [ V , Awaitable [ V ]]], input_values : Union [ Iterable [ T ], Sequence [ T ]], * , chunk_size : Optional [ int ] = None , ignore_exceptions : bool = False , concurrency : Optional [ int ] = None , unordered : bool = False , ) -> Iterable [ Optional [ V ]]: \"\"\"Execute a map operation on an iterable stream. Similar to [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] but uses threads instead of processes. Hence, it is not helpful in CPU-bound situations. A custom parallel map operation supporting both synchronous and `async` map functions. Exceptions encountered in the map function are sent to the host thread where they are either raised (default) or ignored. Each process processes a single chunk at once. Examples: >>> list(threaded_parallel_map(lambda x: x ** 2, [1, 2, 3])) [1, 4, 9] Args: func: The function that should be applied to each element of `input_values`. It can `async`, in that case, a new event loop is started for each chunk. input_values: An iterable of items that `func` is applied to. chunk_size: Tune the number of items processed in each step. Larger numbers result in smaller communication overhead but less parallelism at the start and end. If `chunk_size` has a `__len__` property, the `chunk_size` is calculated automatically if not given. ignore_exceptions: Ignore chunks if `next()` raises an exception on `input_values`. And return `None` if `func` raised an exception in a worker process. concurrency: Number of new threads to start. unordered: Do not preserve the order of the elements, yield them as soon as they have been processed. This decreases the latency caused by difficult-to-process items. Yields: The next result obtained from applying `func` to each input value. May contain `None`-s if `ignore_exceptions=True`. May have different order than the input if `unordered=True`. Raises: WorkerException: If there was an error in the `func` function in a background thread and `ignore_exceptions=False`. \"\"\" config = get_config ( function = func , input_values = input_values , chunk_size = chunk_size , concurrency = concurrency , ) input_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) output_queue : queue . Queue = queue . Queue ( config . concurrency * 2 ) should_stop = threading . Event () threads = [ threading . Thread ( name = f \"threaded_parallel_map_ { config . function_name } _ { i } \" , target = mapper_function , daemon = True , kwargs = dict ( input_queue = input_queue , output_queue = output_queue , should_stop = should_stop , func = func , ), ) for i in range ( config . concurrency ) ] for t in threads : t . start () yield from manage_communication ( input_values = input_values , chunk_size = config . chunk_size , input_queue = input_queue , output_queue = output_queue , unordered = unordered , ignore_exceptions = ignore_exceptions , ) should_stop . set () for t in threads : t . join ( 1 )","title":"threaded_parallel_map()"},{"location":"reference/utilities/#composable-parallel-processing","text":"Because both threaded_parallel_map and parallel_map have a streaming interface, it is easy to compose them and end up with, for example, a process for each CPU core with its own thread-pool or event-loop. Longer pipelines are also easy to imagine. The chunking methods help in these compositions.","title":"Composable parallel processing"},{"location":"reference/utilities/#great_ai.utilities.chunk.chunk","text":"Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length chunk_size except the last one the length of which is between 1 and chunk_size . Useful for parallel processing. Examples: >>> list ( chunk ( range ( 10 ), chunk_size = 3 )) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Parameters: Name Type Description Default values Iterable [ T ] The stream of items to pack into chunks. required chunk_size int Desired length of each (but the last) chunk. required Yields: Type Description Iterable [ List [ T ]] The next chunk. Source code in great_ai/utilities/chunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def chunk ( values : Iterable [ T ], chunk_size : int ) -> Iterable [ List [ T ]]: \"\"\"Turn an iterable of items into an iterable of lists (chunks) of items. Each returned chunk is of length `chunk_size` except the last one the length of which is between 1 and `chunk_size`. Useful for parallel processing. Examples: >>> list(chunk(range(10), chunk_size=3)) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] Args: values: The stream of items to pack into chunks. chunk_size: Desired length of each (but the last) chunk. Yields: The next chunk. \"\"\" assert chunk_size >= 1 result : List [ T ] = [] for v in values : result . append ( v ) if len ( result ) == chunk_size : yield result result = [] if len ( result ) > 0 : yield result","title":"chunk()"},{"location":"reference/utilities/#great_ai.utilities.unchunk.unchunk","text":"Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of chunk . Useful for parallel processing. Similar to itertools.chain but ignores None chunks. Examples: >>> list ( unchunk ([[ 0 , 1 , 2 ], [ 3 , 4 , 5 ], [ 6 , 7 , 8 ], [ 9 ]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Parameters: Name Type Description Default chunks Iterable [ Optional [ Iterable [ T ]]] Stream of chunks to unpack. required Yields: Type Description Iterable [ T ] The next item in the flattened iterable. Source code in great_ai/utilities/unchunk.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 def unchunk ( chunks : Iterable [ Optional [ Iterable [ T ]]]) -> Iterable [ T ]: \"\"\"Turn a stream of chunks of items into a stream of items (flatten operation). The inverse operation of [chunk][great_ai.utilities.chunk.chunk]. Useful for parallel processing. Similar to itertools.chain but ignores `None` chunks. Examples: >>> list(unchunk([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]])) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] Args: chunks: Stream of chunks to unpack. Yields: The next item in the flattened iterable. \"\"\" for chunk in chunks : if chunk is not None : yield from chunk","title":"unchunk()"},{"location":"reference/utilities/#operations","text":"","title":"Operations"},{"location":"reference/utilities/#great_ai.utilities.ConfigFile","text":"Bases: Mapping [ str , str ] A small and safe INI -style configuration loader with dict and ENV support. The values can be accessed using both dot- and index-notation. It is compatible with the dict interface. File format example: # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV : MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. Examples: >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . zeroth_key 'test' >>> ConfigFile ( 'tests/utilities/data/simple.conf' ) . second_key Traceback (most recent call last): ... KeyError : 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile ( 'tests/utilities/data/simple.conf' ) >>> { ** a } {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} Source code in great_ai/utilities/config_file/config_file.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 class ConfigFile ( Mapping [ str , str ]): \"\"\"A small and safe `INI`-style configuration loader with `dict` and `ENV` support. The values can be accessed using both dot- and index-notation. It is compatible with the `dict` interface. File format example: ```toml # comments are allowed everywhere key = value # you can leave or omit whitespace around the equal-sign my_hashtag = \"#great_ai\" # the r-value can be quoted with \" or ' or `. my_var = my_default_value # Default values can be given to env-vars, # see next line. The default value must come first. my_var = ENV:MY_ENV_VAR # If the value starts with the `ENV:` prefix, # it is looked up from the environment variables. ``` Examples: >>> ConfigFile('tests/utilities/data/simple.conf') ConfigFile(path=tests/utilities/data/simple.conf) {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} >>> ConfigFile('tests/utilities/data/simple.conf').zeroth_key 'test' >>> ConfigFile('tests/utilities/data/simple.conf').second_key Traceback (most recent call last): ... KeyError: 'Key `second_key` is not found in configuration file ... >>> a = ConfigFile('tests/utilities/data/simple.conf') >>> {**a} {'zeroth_key': 'test', 'first_key': 'Andr\u00e1s'} \"\"\" ENVIRONMENT_VARIABLE_KEY_PREFIX = \"ENV\" def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse () @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path def _parse ( self ) -> None : with open ( self . _path , encoding = \"utf-8\" ) as f : lines : str = f . read () matches = pattern . findall ( lines ) for key , * values in matches : try : value = next ( v for v in values if v ) except StopIteration : raise ParseError ( f \"\"\"Cannot parse config file ( { self . _path . absolute () } ), error at key ` { key } `\"\"\" ) already_exists = key in self . _key_values if already_exists and not value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): raise KeyError ( f \"Key ` { key } ` has been already defined and its value is ` { self . _key_values [ key ] } `\" ) if value . startswith ( f \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } :\" ): _ , value = value . split ( \":\" ) if value not in os . environ : issue = f \"\"\"The value of ` { key } ` contains the \" { self . ENVIRONMENT_VARIABLE_KEY_PREFIX } ` prefix but ` { value } ` is not defined as an environment variable\"\"\" if already_exists : logger . warning ( f \"\"\" { issue } , using the default value defined above (` { self . _key_values [ key ] } `)\"\"\" ) continue elif self . _ignore_missing : logger . warning ( issue ) else : raise ValueError ( f \" { issue } and no default value has been provided\" ) else : value = os . environ [ value ] self . _key_values [ key ] = value def __getattr__ ( self , key : str ) -> str : if key in self . _key_values : return self . _key_values [ key ] raise KeyError ( f \"Key ` { key } ` is not found in configuration file ( { self . _path . absolute () } )\" ) __getitem__ = __getattr__ def __iter__ ( self ) -> Iterator [ str ]: return iter ( self . _key_values ) def __len__ ( self ) -> int : return len ( self . _key_values ) def keys ( self ) -> KeysView [ str ]: return self . _key_values . keys () def values ( self ) -> ValuesView [ str ]: return self . _key_values . values () def items ( self ) -> ItemsView [ str , str ]: return self . _key_values . items () def __repr__ ( self ) -> str : return f \" { type ( self ) . __name__ } (path= { self . _path . as_posix () } ) { self . _key_values } \"","title":"ConfigFile"},{"location":"reference/utilities/#great_ai.utilities.config_file.config_file.ConfigFile.__init__","text":"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Parameters: Name Type Description Default path Union [ Path , str ] Local path of the configuration file. required ignore_missing bool Don't raise an exception on missing environment variables. False Raises: Type Description FileNotFoundError If there is no file at the specified path. ParseError If the provided file does not conform to the expected format. KeyError If there is duplication in the keys. ValueError If an environment variable is referenced but it is not set in the system and ignore_missing=False . Source code in great_ai/utilities/config_file/config_file.py 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 def __init__ ( self , path : Union [ Path , str ], * , ignore_missing : bool = False ) -> None : \"\"\"Load and parse a configuration file. Everything is eager-loaded, thus, exceptions may be thrown here. Args: path: Local path of the configuration file. ignore_missing: Don't raise an exception on missing environment variables. Raises: FileNotFoundError: If there is no file at the specified path. ParseError: If the provided file does not conform to the expected format. KeyError: If there is duplication in the keys. ValueError: If an environment variable is referenced but it is not set in the system and `ignore_missing=False`. \"\"\" if not isinstance ( path , Path ): path = Path ( path ) if not path . exists (): raise FileNotFoundError ( path . absolute ()) self . _ignore_missing = ignore_missing self . _path = path self . _key_values : Dict [ str , str ] = {} self . _parse ()","title":"__init__()"},{"location":"reference/utilities/#great_ai.utilities.config_file.config_file.ConfigFile.path","text":"Original path from where the configuration was loaded. Source code in great_ai/utilities/config_file/config_file.py 83 84 85 86 @property def path ( self ) -> Path : \"\"\"Original path from where the configuration was loaded.\"\"\" return self . _path","title":"path()"},{"location":"reference/utilities/#great_ai.utilities.get_logger","text":"Return a customised logger used throughout the GreatAI codebase. Uses colors, and only prints timestamps when not running inside notebook. Source code in great_ai/utilities/logger/get_logger.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def get_logger ( name : str , level : int = logging . INFO , disable_colors : bool = False ) -> logging . Logger : \"\"\"Return a customised logger used throughout the GreatAI codebase. Uses colors, and only prints timestamps when not running inside notebook. \"\"\" if name not in loggers : logger = logging . getLogger ( name ) logger . setLevel ( level ) try : get_ipython () # type: ignore log_format = \" %(message)s \" except NameError : # will fail outside of a notebook https://ipython.org/ log_format = \" %(asctime)s | %(levelname)8s | %(message)s \" stdout_handler = logging . StreamHandler () stdout_handler . setLevel ( level ) if not disable_colors : stdout_handler . setFormatter ( CustomFormatter ( log_format )) logger . addHandler ( stdout_handler ) loggers [ name ] = logger return loggers [ name ]","title":"get_logger()"},{"location":"reference/views/","text":"View models # Trace # Bases: Generic [ T ] , HashableBaseModel Source code in great_ai/views/trace.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 class Trace ( Generic [ T ], HashableBaseModel ): trace_id : str created : str original_execution_time_ms : float logged_values : Dict [ str , Any ] models : List [ Model ] exception : Optional [ str ] output : Optional [ T ] feedback : Any = None tags : List [ str ] class Config : extra = Extra . ignore @property def input ( self ) -> Any : return ( self . logged_values [ \"input\" ] if list ( self . logged_values . keys ()) == [ \"input\" ] else self . logged_values ) @property def models_flat ( self ) -> str : return \", \" . join ( f \" { m . key } : { m . version } \" for m in self . models ) @property def output_flat ( self ) -> str : return pformat ( self . output , indent = 2 , compact = True ) @property def exception_flat ( self ) -> str : return ( \"null\" if self . exception is None else pformat ( self . exception , indent = 2 , compact = True ) ) @property def feedback_flat ( self ) -> str : return ( \"null\" if self . feedback is None else pformat ( self . feedback , indent = 2 , compact = True ) ) @property def tags_flat ( self ) -> str : return \", \\n \" . join ( self . tags ) def to_flat_dict ( self , include_original : bool = True ) -> Dict [ str , Any ]: return { ** ( self . dict () if include_original else { \"trace_id\" : self . trace_id , \"created\" : self . created , \"original_execution_time_ms\" : self . original_execution_time_ms , } ), ** { k : pformat ( v , indent = 2 , compact = True ) for k , v in self . logged_values . items () }, \"models_flat\" : self . models_flat , \"exception_flat\" : self . exception_flat , \"output_flat\" : self . output_flat , \"feedback_flat\" : self . feedback_flat , \"tags_flat\" : self . tags_flat , } def __repr__ ( self ) -> str : return f \"\"\"Trace[ { type ( self . output ) . __name__ } ]( { pformat ( self . dict (), indent = 2 , compact = True ) . replace ( '{ ' , '{' , 1 ) } )\"\"\" RouteConfig # Bases: BaseModel Source code in great_ai/views/route_config.py 4 5 6 7 8 9 10 class RouteConfig ( BaseModel ): prediction_endpoint_enabled : bool = True docs_endpoints_enabled : bool = True dashboard_enabled : bool = True feedback_endpoints_enabled : bool = True trace_endpoints_enabled : bool = True meta_endpoints_enabled : bool = True ClassificationOutput # Bases: HashableBaseModel Source code in great_ai/views/outputs/classification_output.py 6 7 8 9 class ClassificationOutput ( HashableBaseModel ): label : Union [ str , int ] confidence : float explanation : Optional [ Any ] MultiLabelClassificationOutput # Bases: HashableBaseModel Source code in great_ai/views/outputs/multi_label_classification_output.py 7 8 class MultiLabelClassificationOutput ( HashableBaseModel ): labels : List [ ClassificationOutput ] = [] RegressionOutput # Bases: HashableBaseModel Source code in great_ai/views/outputs/regression_output.py 6 7 8 class RegressionOutput ( HashableBaseModel ): value : Union [ int , float ] explanation : Optional [ Any ] SequenceLabelingOutput # Bases: HashableBaseModel Source code in great_ai/views/outputs/sequence_labeling_output.py 13 14 15 class SequenceLabelingOutput ( HashableBaseModel ): labeled_tokens : List [ LabeledToken ] explanation : Optional [ Any ]","title":"View models"},{"location":"reference/views/#view-models","text":"","title":"View models"},{"location":"reference/views/#great_ai.Trace","text":"Bases: Generic [ T ] , HashableBaseModel Source code in great_ai/views/trace.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 class Trace ( Generic [ T ], HashableBaseModel ): trace_id : str created : str original_execution_time_ms : float logged_values : Dict [ str , Any ] models : List [ Model ] exception : Optional [ str ] output : Optional [ T ] feedback : Any = None tags : List [ str ] class Config : extra = Extra . ignore @property def input ( self ) -> Any : return ( self . logged_values [ \"input\" ] if list ( self . logged_values . keys ()) == [ \"input\" ] else self . logged_values ) @property def models_flat ( self ) -> str : return \", \" . join ( f \" { m . key } : { m . version } \" for m in self . models ) @property def output_flat ( self ) -> str : return pformat ( self . output , indent = 2 , compact = True ) @property def exception_flat ( self ) -> str : return ( \"null\" if self . exception is None else pformat ( self . exception , indent = 2 , compact = True ) ) @property def feedback_flat ( self ) -> str : return ( \"null\" if self . feedback is None else pformat ( self . feedback , indent = 2 , compact = True ) ) @property def tags_flat ( self ) -> str : return \", \\n \" . join ( self . tags ) def to_flat_dict ( self , include_original : bool = True ) -> Dict [ str , Any ]: return { ** ( self . dict () if include_original else { \"trace_id\" : self . trace_id , \"created\" : self . created , \"original_execution_time_ms\" : self . original_execution_time_ms , } ), ** { k : pformat ( v , indent = 2 , compact = True ) for k , v in self . logged_values . items () }, \"models_flat\" : self . models_flat , \"exception_flat\" : self . exception_flat , \"output_flat\" : self . output_flat , \"feedback_flat\" : self . feedback_flat , \"tags_flat\" : self . tags_flat , } def __repr__ ( self ) -> str : return f \"\"\"Trace[ { type ( self . output ) . __name__ } ]( { pformat ( self . dict (), indent = 2 , compact = True ) . replace ( '{ ' , '{' , 1 ) } )\"\"\"","title":"Trace"},{"location":"reference/views/#great_ai.RouteConfig","text":"Bases: BaseModel Source code in great_ai/views/route_config.py 4 5 6 7 8 9 10 class RouteConfig ( BaseModel ): prediction_endpoint_enabled : bool = True docs_endpoints_enabled : bool = True dashboard_enabled : bool = True feedback_endpoints_enabled : bool = True trace_endpoints_enabled : bool = True meta_endpoints_enabled : bool = True","title":"RouteConfig"},{"location":"reference/views/#great_ai.ClassificationOutput","text":"Bases: HashableBaseModel Source code in great_ai/views/outputs/classification_output.py 6 7 8 9 class ClassificationOutput ( HashableBaseModel ): label : Union [ str , int ] confidence : float explanation : Optional [ Any ]","title":"ClassificationOutput"},{"location":"reference/views/#great_ai.MultiLabelClassificationOutput","text":"Bases: HashableBaseModel Source code in great_ai/views/outputs/multi_label_classification_output.py 7 8 class MultiLabelClassificationOutput ( HashableBaseModel ): labels : List [ ClassificationOutput ] = []","title":"MultiLabelClassificationOutput"},{"location":"reference/views/#great_ai.RegressionOutput","text":"Bases: HashableBaseModel Source code in great_ai/views/outputs/regression_output.py 6 7 8 class RegressionOutput ( HashableBaseModel ): value : Union [ int , float ] explanation : Optional [ Any ]","title":"RegressionOutput"},{"location":"reference/views/#great_ai.SequenceLabelingOutput","text":"Bases: HashableBaseModel Source code in great_ai/views/outputs/sequence_labeling_output.py 13 14 15 class SequenceLabelingOutput ( HashableBaseModel ): labeled_tokens : List [ LabeledToken ] explanation : Optional [ Any ]","title":"SequenceLabelingOutput"},{"location":"tutorial/","text":"Train and deploy a SOTA model # Let's see great-ai in action by going over the life-cycle of a simple service. Objectives # You will see how the great_ai.utilities can integrate into your Data Science workflow. You will use great_ai.large_file to version and store your trained model. You will use GreatAI to prepare your model for a robust and responsible deployment. Overview # You are going to train a field of study (domain) classifier for scientific sentences. The exact task was proposed by the SciBERT paper in which SciBERT achieved an F1-score of 0.6571 . We are going to outperform it using a trivial text classification model: a Linear SVM . We use the same synthetic dataset derived from the Microsoft Academic Graph . The dataset is available here . Success You are ready to start the tutorial. Feel free to come back to the summary section once you're finished. Train it Deploy it Summary # Training notebook # We load and preprocess the dataset while relying on great_ai.utilities.clean for doing the heavy-lifting. Additionally, the preprocessing is parallelised using great_ai.utilities.simple_parallel_map After training and evaluating a model, it is exported using great_ai.save_model . Remote storage To store your model remotely, you need to set your credentials before calling save_model . For example, to use AWS S3 : from great_ai.large_file import LargeFileS3 LargeFileS3 . configure ( aws_region_name = 'eu-west-2' , aws_access_key_id = 'MY_AWS_ACCESS_KEY' , aws_secret_access_key = 'MY_AWS_SECRET_KEY' , large_files_bucket_name = 'my_bucket_for_models' ) from great_ai import save_model save_model ( model , key = 'my-domain-predictor' ) For more info, checkout the configuration how-to page . Deployment notebook # We create an inference function that can be hardened by wrapping it in a GreatAI instance. from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( 'my-domain-predictor' ) #(1) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) @use_model loads and injects your model into the predict_domain function's model argument. You can freely reference it knowing that the function is always provided with it. Finally, we test the model's inference function through the GreatAI dashboard. The only thing left is to deploy the hardened service properly. Learn about all the features Look at more examples","title":"Tutorial overview"},{"location":"tutorial/#train-and-deploy-a-sota-model","text":"Let's see great-ai in action by going over the life-cycle of a simple service.","title":"Train and deploy a SOTA model"},{"location":"tutorial/#objectives","text":"You will see how the great_ai.utilities can integrate into your Data Science workflow. You will use great_ai.large_file to version and store your trained model. You will use GreatAI to prepare your model for a robust and responsible deployment.","title":"Objectives"},{"location":"tutorial/#overview","text":"You are going to train a field of study (domain) classifier for scientific sentences. The exact task was proposed by the SciBERT paper in which SciBERT achieved an F1-score of 0.6571 . We are going to outperform it using a trivial text classification model: a Linear SVM . We use the same synthetic dataset derived from the Microsoft Academic Graph . The dataset is available here . Success You are ready to start the tutorial. Feel free to come back to the summary section once you're finished. Train it Deploy it","title":"Overview"},{"location":"tutorial/#summary","text":"","title":"Summary"},{"location":"tutorial/#training-notebook","text":"We load and preprocess the dataset while relying on great_ai.utilities.clean for doing the heavy-lifting. Additionally, the preprocessing is parallelised using great_ai.utilities.simple_parallel_map After training and evaluating a model, it is exported using great_ai.save_model . Remote storage To store your model remotely, you need to set your credentials before calling save_model . For example, to use AWS S3 : from great_ai.large_file import LargeFileS3 LargeFileS3 . configure ( aws_region_name = 'eu-west-2' , aws_access_key_id = 'MY_AWS_ACCESS_KEY' , aws_secret_access_key = 'MY_AWS_SECRET_KEY' , large_files_bucket_name = 'my_bucket_for_models' ) from great_ai import save_model save_model ( model , key = 'my-domain-predictor' ) For more info, checkout the configuration how-to page .","title":"Training notebook"},{"location":"tutorial/#deployment-notebook","text":"We create an inference function that can be hardened by wrapping it in a GreatAI instance. from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( 'my-domain-predictor' ) #(1) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) @use_model loads and injects your model into the predict_domain function's model argument. You can freely reference it knowing that the function is always provided with it. Finally, we test the model's inference function through the GreatAI dashboard. The only thing left is to deploy the hardened service properly. Learn about all the features Look at more examples","title":"Deployment notebook"},{"location":"tutorial/deploy/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); const range = document.createRange(); range.selectNodeContents(node); selection.addRange(range); document.execCommand('copy'); selection.removeAllRanges(); return Promise.resolve(); } function copyText(text) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(text); } const body = document.body; if (!body) { return Promise.reject(new Error()); } const node = createNode(text); body.appendChild(node); copyNode(node); body.removeChild(node); return Promise.resolve(); } function copy(button) { const id = button.getAttribute('for'); const text = button.getAttribute('value'); function trigger() { button.dispatchEvent(new CustomEvent('clipboard-copy', { bubbles: true })); } if (text) { copyText(text).then(trigger); } else if (id) { const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument; if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return; const node = root.getElementById(id); if (node) copyTarget(node).then(trigger); } } function copyTarget(content) { if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) { return copyText(content.value); } else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) { return copyText(content.href); } else { return copyNode(content); } } function clicked(event) { const button = event.currentTarget; if (button instanceof HTMLElement) { copy(button); } } function keydown(event) { if (event.key === ' ' || event.key === 'Enter') { const button = event.currentTarget; if (button instanceof HTMLElement) { event.preventDefault(); copy(button); } } } function focused(event) { event.currentTarget.addEventListener('keydown', keydown); } function blurred(event) { event.currentTarget.removeEventListener('keydown', keydown); } class ClipboardCopyElement extends HTMLElement { constructor() { super(); this.addEventListener('click', clicked); this.addEventListener('focus', focused); this.addEventListener('blur', blurred); } connectedCallback() { if (!this.hasAttribute('tabindex')) { this.setAttribute('tabindex', '0'); } if (!this.hasAttribute('role')) { this.setAttribute('role', 'button'); } } get value() { return this.getAttribute('value') || ''; } set value(text) { this.setAttribute('value', text); } } if (!window.customElements.get('clipboard-copy')) { window.ClipboardCopyElement = ClipboardCopyElement; window.customElements.define('clipboard-copy', ClipboardCopyElement); } return ClipboardCopyElement; })); document.addEventListener('clipboard-copy', function(event) { const notice = event.target.querySelector('.notice') notice.hidden = false setTimeout(function() { notice.hidden = true }, 1000) }) pre { line-height: 125%; } td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Harden and deploy your app \u00b6 Finally, it's time to deploy your model. But before that, you have to make sure you follow AI deployment best-practices . In the past, this step was too often either the source of unexpected struggle, or worse, simply ignored. With GreatAI , it has become a matter of 4 lines of code. In [1]: Copied! from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( \"my-domain-predictor\" ) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) from great_ai import GreatAI, use_model from great_ai.utilities import clean @GreatAI.create @use_model(\"my-domain-predictor\") def predict_domain(sentence, model): inputs = [clean(sentence)] return str(model.predict(inputs)[0]) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.4): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of my-domain-predictor Latest version of my-domain-predictor is 9 (from versions: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9) File my-domain-predictor-9 found in cache In [2]: Copied! predict_domain ( \"Mountains are just big rocks.\" ) predict_domain(\"Mountains are just big rocks.\") Out[2]: Trace[str]({'created': '2022-07-12T13:34:26.743292', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 29, 'arg:sentence:value': 'Mountains are just big rocks.'}, 'models': [{'key': 'my-domain-predictor', 'version': 9}], 'original_execution_time_ms': 6.9699, 'output': 'geography', 'tags': ['predict_domain', 'online', 'development'], 'trace_id': 'c80bdee3-602b-49dd-a84d-6eef80127e5a'}) Notice how the original return value is under the .output key. Additionally, a plethora of metadata has been added which will be useful later on. Running your app in development-mode is as easy as executing great-ai deploy.ipynb from your terminal. In [3]: Copied! ! great - ai deploy . ipynb # leave this running and open http://127.0.0.1:6060 !great-ai deploy.ipynb # leave this running and open http://127.0.0.1:6060 2022-07-12 15:34:28 | INFO | Converting notebook to Python script 2022-07-12 15:34:29 | INFO | Found `predict_domain` to be the GreatAI app 2022-07-12 15:34:29 | INFO | Uvicorn running on http://0.0.0.0:6060 (Press CTRL+C to quit) 2022-07-12 15:34:31 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-07-12 15:34:31 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver 2022-07-12 15:34:31 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production 2022-07-12 15:34:31 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal 2022-07-12 15:34:31 | INFO | GreatAI (v0.1.4): configured \u2705 2022-07-12 15:34:31 | INFO | \ud83d\udd29 tracing_database: ParallelTinyDbDriver 2022-07-12 15:34:31 | INFO | \ud83d\udd29 large_file_implementation: LargeFileLocal 2022-07-12 15:34:31 | INFO | \ud83d\udd29 is_production: False 2022-07-12 15:34:31 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-07-12 15:34:31 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-07-12 15:34:31 | INFO | \ud83d\udd29 dashboard_table_size: 50 2022-07-12 15:34:31 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-07-12 15:34:31 | WARNING | > Find out more at https://se-ml.github.io/practices 2022-07-12 15:34:31 | INFO | Fetching cached versions of my-domain-predictor 2022-07-12 15:34:31 | INFO | Latest version of my-domain-predictor is 9 (from versions: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9) 2022-07-12 15:34:31 | INFO | File my-domain-predictor-9 found in cache 2022-07-12 15:34:31 | INFO | Started server process [199794] 2022-07-12 15:34:31 | INFO | Waiting for application startup. 2022-07-12 15:34:31 | INFO | Application startup complete. ^C 2022-07-12 15:34:33 | INFO | Shutting down 2022-07-12 15:34:33 | INFO | Waiting for application shutdown. 2022-07-12 15:34:33 | INFO | Application shutdown complete. 2022-07-12 15:34:33 | INFO | Finished server process [199794] Congrats, you've just created your first GreatAI service! \ud83c\udf89 Now that you've made sure your application is hardened enough for the intended use case, it is time to deploy it. The responsibilities of GreatAI end when it wraps your inference function and model into a production-ready service. You're given the freedom and responsibility to deploy this service. Fortunately, you (or your organisation) probably already have an established routine for deploying services. There are three main approaches to deploy a GreatAI service: For more info about them, check out the deployment how-to . For more thorough examples, see the examples page . Go back to the summary \u00b6","title":"Harden and deploy your app"},{"location":"tutorial/deploy/#harden-and-deploy-your-app","text":"Finally, it's time to deploy your model. But before that, you have to make sure you follow AI deployment best-practices . In the past, this step was too often either the source of unexpected struggle, or worse, simply ignored. With GreatAI , it has become a matter of 4 lines of code. In [1]: Copied! from great_ai import GreatAI , use_model from great_ai.utilities import clean @GreatAI . create @use_model ( \"my-domain-predictor\" ) def predict_domain ( sentence , model ): inputs = [ clean ( sentence )] return str ( model . predict ( inputs )[ 0 ]) from great_ai import GreatAI, use_model from great_ai.utilities import clean @GreatAI.create @use_model(\"my-domain-predictor\") def predict_domain(sentence, model): inputs = [clean(sentence)] return str(model.predict(inputs)[0]) Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.4): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of my-domain-predictor Latest version of my-domain-predictor is 9 (from versions: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9) File my-domain-predictor-9 found in cache In [2]: Copied! predict_domain ( \"Mountains are just big rocks.\" ) predict_domain(\"Mountains are just big rocks.\") Out[2]: Trace[str]({'created': '2022-07-12T13:34:26.743292', 'exception': None, 'feedback': None, 'logged_values': { 'arg:sentence:length': 29, 'arg:sentence:value': 'Mountains are just big rocks.'}, 'models': [{'key': 'my-domain-predictor', 'version': 9}], 'original_execution_time_ms': 6.9699, 'output': 'geography', 'tags': ['predict_domain', 'online', 'development'], 'trace_id': 'c80bdee3-602b-49dd-a84d-6eef80127e5a'}) Notice how the original return value is under the .output key. Additionally, a plethora of metadata has been added which will be useful later on. Running your app in development-mode is as easy as executing great-ai deploy.ipynb from your terminal. In [3]: Copied! ! great - ai deploy . ipynb # leave this running and open http://127.0.0.1:6060 !great-ai deploy.ipynb # leave this running and open http://127.0.0.1:6060 2022-07-12 15:34:28 | INFO | Converting notebook to Python script 2022-07-12 15:34:29 | INFO | Found `predict_domain` to be the GreatAI app 2022-07-12 15:34:29 | INFO | Uvicorn running on http://0.0.0.0:6060 (Press CTRL+C to quit) 2022-07-12 15:34:31 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f 2022-07-12 15:34:31 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver 2022-07-12 15:34:31 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production 2022-07-12 15:34:31 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal 2022-07-12 15:34:31 | INFO | GreatAI (v0.1.4): configured \u2705 2022-07-12 15:34:31 | INFO | \ud83d\udd29 tracing_database: ParallelTinyDbDriver 2022-07-12 15:34:31 | INFO | \ud83d\udd29 large_file_implementation: LargeFileLocal 2022-07-12 15:34:31 | INFO | \ud83d\udd29 is_production: False 2022-07-12 15:34:31 | INFO | \ud83d\udd29 should_log_exception_stack: True 2022-07-12 15:34:31 | INFO | \ud83d\udd29 prediction_cache_size: 512 2022-07-12 15:34:31 | INFO | \ud83d\udd29 dashboard_table_size: 50 2022-07-12 15:34:31 | WARNING | You still need to check whether you follow all best practices before trusting your deployment. 2022-07-12 15:34:31 | WARNING | > Find out more at https://se-ml.github.io/practices 2022-07-12 15:34:31 | INFO | Fetching cached versions of my-domain-predictor 2022-07-12 15:34:31 | INFO | Latest version of my-domain-predictor is 9 (from versions: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9) 2022-07-12 15:34:31 | INFO | File my-domain-predictor-9 found in cache 2022-07-12 15:34:31 | INFO | Started server process [199794] 2022-07-12 15:34:31 | INFO | Waiting for application startup. 2022-07-12 15:34:31 | INFO | Application startup complete. ^C 2022-07-12 15:34:33 | INFO | Shutting down 2022-07-12 15:34:33 | INFO | Waiting for application shutdown. 2022-07-12 15:34:33 | INFO | Application shutdown complete. 2022-07-12 15:34:33 | INFO | Finished server process [199794] Congrats, you've just created your first GreatAI service! \ud83c\udf89 Now that you've made sure your application is hardened enough for the intended use case, it is time to deploy it. The responsibilities of GreatAI end when it wraps your inference function and model into a production-ready service. You're given the freedom and responsibility to deploy this service. Fortunately, you (or your organisation) probably already have an established routine for deploying services. There are three main approaches to deploy a GreatAI service: For more info about them, check out the deployment how-to . For more thorough examples, see the examples page .","title":"Harden and deploy your app"},{"location":"tutorial/deploy/#go-back-to-the-summary","text":"","title":"Go back to the summary"},{"location":"tutorial/train/","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Train your model \u00b6 The first step is, of course, to install great-ai in your Python environment. In [1]: Copied! % pip install great - ai > / dev / null %pip install great-ai > /dev/null Note: you may need to restart the kernel to use updated packages. First, we have to get some data. After downloading it from here , we might notice that the dataset is in JSON Lines format (each line is a separate JSON document). Let's write a function which takes a single line and returns the sentence and the corresponding label from it. Before returning, the sentence is also cleaned to remove any LaTeX, XML, unicode, PDF-extraction artifacts. In [2]: Copied! import json from great_ai.utilities import clean def preprocess ( line ): data_point = json . loads ( line ) sentence = data_point [ \"text\" ] label = data_point [ \"label\" ] return clean ( sentence ), label import json from great_ai.utilities import clean def preprocess(line): data_point = json.loads(line) sentence = data_point[\"text\"] label = data_point[\"label\"] return clean(sentence), label Now, we can load the dataset and extract the training samples from it. Since we're impatient, we can do it in parallel using the simple_parallel_map function. Open files in Python are iterable: in text mode, each iteration returns the next line. In [3]: Copied! from great_ai.utilities import simple_parallel_map with open ( \"data/train.txt\" , encoding = \"utf-8\" ) as f : training_data = simple_parallel_map ( preprocess , f ) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] from great_ai.utilities import simple_parallel_map with open(\"data/train.txt\", encoding=\"utf-8\") as f: training_data = simple_parallel_map(preprocess, f) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84000/84000 [00:09<00:00, 8960.63it/s] Let's do the same for the test data. In [4]: Copied! with open ( \"data/test.txt\" , encoding = \"utf-8\" ) as f : test_data = simple_parallel_map ( preprocess , f ) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] with open(\"data/test.txt\", encoding=\"utf-8\") as f: test_data = simple_parallel_map(preprocess, f) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22399/22399 [00:03<00:00, 6078.02it/s] After obtaining some clean data, it's time to create and train a model on it. We are going to use scikit-learn for this purpose. In this case, we opted for a linear Support Vector Machine because it's known to be an adequate baseline for simple classification tasks. In [5]: Copied! from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline ( TfidfVectorizer (), LinearSVC ()) # todo: hyperparameter-optimisation model . fit ( X_train , y_train ) from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline(TfidfVectorizer(), LinearSVC()) # todo: hyperparameter-optimisation model.fit(X_train, y_train) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) TfidfVectorizer TfidfVectorizer() LinearSVC LinearSVC() It's already finished. Let's see how well it performs. But first, we need to configure matplotlib to make the charts more legible. ConfusionMatrixDisplay relies on matplotlib for rendering. In [6]: Copied! import matplotlib.pyplot as plt % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 import matplotlib.pyplot as plt %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 Next, we check the quality of the model on the test split. We can use the classification report to check the common metrics, such as the macro F1-score. We also draw the confusion matrix to get some insights into the types of mistakes being made by the model. In [7]: Copied! from sklearn import metrics y_predicted = model . predict ( X_test ) print ( metrics . classification_report ( y_test , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_test , y_predicted ) None from sklearn import metrics y_predicted = model.predict(X_test) print(metrics.classification_report(y_test, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions(y_test, y_predicted) None precision recall f1-score support business 0.67 0.69 0.68 3198 economics 0.69 0.71 0.70 3189 geography 0.70 0.73 0.72 3207 medicine 0.90 0.91 0.90 3187 politics 0.63 0.57 0.60 3169 psychology 0.73 0.73 0.73 3252 sociology 0.51 0.51 0.51 3197 accuracy 0.69 22399 macro avg 0.69 0.69 0.69 22399 weighted avg 0.69 0.69 0.69 22399 Great work, we can be rightfully satisfied with our model. Seeing the results, we achieved an F1-score of 0.69 which is about 5% better than SciBERT's 0.6571! You might wonder that \"this is great, but besides some utility functions ( clean , simple_parallel_map , ...) what more value does GreatAI add?\" . This would be a valid argument because the scope of GreatAI actually only starts here. Not coincidentally, this is the point where the scope of Data Science ends but it's still a grey zone for software engineering. In order to use this model in production, we have to make it available on some possibly shared infrastructure. In [8]: Copied! from great_ai import save_model save_model ( model , key = \"my-domain-predictor\" ) from great_ai import save_model save_model(model, key=\"my-domain-predictor\") Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.4): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of my-domain-predictor Copying file for my-domain-predictor-9 Compressing my-domain-predictor-9 Model my-domain-predictor uploaded with version 9 Out[8]: 'my-domain-predictor:9' Let's continue by finishing the deployment in the next notebook. \u00b6","title":"Train your model"},{"location":"tutorial/train/#train-your-model","text":"The first step is, of course, to install great-ai in your Python environment. In [1]: Copied! % pip install great - ai > / dev / null %pip install great-ai > /dev/null Note: you may need to restart the kernel to use updated packages. First, we have to get some data. After downloading it from here , we might notice that the dataset is in JSON Lines format (each line is a separate JSON document). Let's write a function which takes a single line and returns the sentence and the corresponding label from it. Before returning, the sentence is also cleaned to remove any LaTeX, XML, unicode, PDF-extraction artifacts. In [2]: Copied! import json from great_ai.utilities import clean def preprocess ( line ): data_point = json . loads ( line ) sentence = data_point [ \"text\" ] label = data_point [ \"label\" ] return clean ( sentence ), label import json from great_ai.utilities import clean def preprocess(line): data_point = json.loads(line) sentence = data_point[\"text\"] label = data_point[\"label\"] return clean(sentence), label Now, we can load the dataset and extract the training samples from it. Since we're impatient, we can do it in parallel using the simple_parallel_map function. Open files in Python are iterable: in text mode, each iteration returns the next line. In [3]: Copied! from great_ai.utilities import simple_parallel_map with open ( \"data/train.txt\" , encoding = \"utf-8\" ) as f : training_data = simple_parallel_map ( preprocess , f ) X_train = [ d [ 0 ] for d in training_data ] y_train = [ d [ 1 ] for d in training_data ] from great_ai.utilities import simple_parallel_map with open(\"data/train.txt\", encoding=\"utf-8\") as f: training_data = simple_parallel_map(preprocess, f) X_train = [d[0] for d in training_data] y_train = [d[1] for d in training_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84000/84000 [00:09<00:00, 8960.63it/s] Let's do the same for the test data. In [4]: Copied! with open ( \"data/test.txt\" , encoding = \"utf-8\" ) as f : test_data = simple_parallel_map ( preprocess , f ) X_test = [ d [ 0 ] for d in test_data ] y_test = [ d [ 1 ] for d in test_data ] with open(\"data/test.txt\", encoding=\"utf-8\") as f: test_data = simple_parallel_map(preprocess, f) X_test = [d[0] for d in test_data] y_test = [d[1] for d in test_data] 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 22399/22399 [00:03<00:00, 6078.02it/s] After obtaining some clean data, it's time to create and train a model on it. We are going to use scikit-learn for this purpose. In this case, we opted for a linear Support Vector Machine because it's known to be an adequate baseline for simple classification tasks. In [5]: Copied! from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline ( TfidfVectorizer (), LinearSVC ()) # todo: hyperparameter-optimisation model . fit ( X_train , y_train ) from sklearn.pipeline import make_pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC model = make_pipeline(TfidfVectorizer(), LinearSVC()) # todo: hyperparameter-optimisation model.fit(X_train, y_train) Out[5]: #sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;} Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. Pipeline Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()), ('linearsvc', LinearSVC())]) TfidfVectorizer TfidfVectorizer() LinearSVC LinearSVC() It's already finished. Let's see how well it performs. But first, we need to configure matplotlib to make the charts more legible. ConfusionMatrixDisplay relies on matplotlib for rendering. In [6]: Copied! import matplotlib.pyplot as plt % matplotlib inline plt . rcParams [ \"figure.figsize\" ] = ( 30 , 15 ) plt . rcParams [ \"figure.facecolor\" ] = \"white\" plt . rcParams [ \"font.size\" ] = 12 import matplotlib.pyplot as plt %matplotlib inline plt.rcParams[\"figure.figsize\"] = (30, 15) plt.rcParams[\"figure.facecolor\"] = \"white\" plt.rcParams[\"font.size\"] = 12 Next, we check the quality of the model on the test split. We can use the classification report to check the common metrics, such as the macro F1-score. We also draw the confusion matrix to get some insights into the types of mistakes being made by the model. In [7]: Copied! from sklearn import metrics y_predicted = model . predict ( X_test ) print ( metrics . classification_report ( y_test , y_predicted )) metrics . ConfusionMatrixDisplay . from_predictions ( y_test , y_predicted ) None from sklearn import metrics y_predicted = model.predict(X_test) print(metrics.classification_report(y_test, y_predicted)) metrics.ConfusionMatrixDisplay.from_predictions(y_test, y_predicted) None precision recall f1-score support business 0.67 0.69 0.68 3198 economics 0.69 0.71 0.70 3189 geography 0.70 0.73 0.72 3207 medicine 0.90 0.91 0.90 3187 politics 0.63 0.57 0.60 3169 psychology 0.73 0.73 0.73 3252 sociology 0.51 0.51 0.51 3197 accuracy 0.69 22399 macro avg 0.69 0.69 0.69 22399 weighted avg 0.69 0.69 0.69 22399 Great work, we can be rightfully satisfied with our model. Seeing the results, we achieved an F1-score of 0.69 which is about 5% better than SciBERT's 0.6571! You might wonder that \"this is great, but besides some utility functions ( clean , simple_parallel_map , ...) what more value does GreatAI add?\" . This would be a valid argument because the scope of GreatAI actually only starts here. Not coincidentally, this is the point where the scope of Data Science ends but it's still a grey zone for software engineering. In order to use this model in production, we have to make it available on some possibly shared infrastructure. In [8]: Copied! from great_ai import save_model save_model ( model , key = \"my-domain-predictor\" ) from great_ai import save_model save_model(model, key=\"my-domain-predictor\") Environment variable ENVIRONMENT is not set, defaulting to development mode \u203c\ufe0f Cannot find credentials files, defaulting to using ParallelTinyDbDriver The selected tracing database (ParallelTinyDbDriver) is not recommended for production Cannot find credentials files, defaulting to using LargeFileLocal GreatAI (v0.1.4): configured \u2705 \ud83d\udd29 tracing_database: ParallelTinyDbDriver \ud83d\udd29 large_file_implementation: LargeFileLocal \ud83d\udd29 is_production: False \ud83d\udd29 should_log_exception_stack: True \ud83d\udd29 prediction_cache_size: 512 \ud83d\udd29 dashboard_table_size: 50 You still need to check whether you follow all best practices before trusting your deployment. > Find out more at https://se-ml.github.io/practices Fetching cached versions of my-domain-predictor Copying file for my-domain-predictor-9 Compressing my-domain-predictor-9 Model my-domain-predictor uploaded with version 9 Out[8]: 'my-domain-predictor:9'","title":"Train your model"},{"location":"tutorial/train/#lets-continue-by-finishing-the-deployment-in-the-next-notebook","text":"","title":"Let's continue by finishing the deployment in the next notebook."}]}
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GIT binary patch
delta 14
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diff --git a/thesis/chapters/0_abstract.tex b/thesis/chapters/0_abstract.tex
index 864fd4f..49e7a8a 100644
--- a/thesis/chapters/0_abstract.tex
+++ b/thesis/chapters/0_abstract.tex
@@ -1,11 +1,11 @@
 \begin{abstract}
 
 \absdiv{Background}
-Despite its long-standing history, artificial intelligence (AI) has only recently started enjoying widespread industry awareness and adoption; partly thanks to the prevalence of accessible frameworks exposing state-of-the-art models through simple API-s. In order to achieve robust production deployments, the successful integration of AI components demands strong engineering methods. Concerningly, a tendency seems to be unfolding: even though industry professionals already have access to frameworks for deploying AI correctly and responsibly, case-studies and developer surveys have found that a large fraction of deployments do not follow best practices.
+Despite its long-standing history, artificial intelligence (AI) has only recently started enjoying widespread industry awareness and adoption; partly thanks to the prevalence of frameworks accessibly exposing state-of-the-art models. In order to achieve robust production deployments, the successful integration of AI components demands strong engineering methods. Concerningly, a tendency seems to be unfolding: even though professionals already have access to frameworks for deploying AI correctly and responsibly, case studies and developer surveys have found that a large fraction of deployments do not follow best practices.
 \absdiv{Objective}
 This thesis sets out to investigate the reasons behind the asymmetry between the adoption of accessible AI libraries and existing reusable solutions to robust deployments. A software framework called \textit{GreatAI} is designed which aims to facilitate \underline{G}eneral \underline{R}obust \underline{E}nd-to-end \underline{A}utomated \underline{T}rustworthy AI deployments while attempting to overcome the practical drawbacks of its predecessors.
 \absdiv{Method}
-The utility of \textit{GreatAI} is validated using the principles of design science methodology through iteratively designing its API and implementation along with the text mining pipeline of a commercial product. Subsequently, interviews are conducted with practitioners for validating the generalisability of the design.
+The utility of \textit{GreatAI} is validated using the principles of design science methodology through iteratively designing its API and implementation in a case study of a commercial text mining pipeline. Subsequently, interviews are conducted with practitioners for validating the generalisability of the design.
 \absdiv{Results}
 To do.
 \absdiv{Conclusions}
diff --git a/thesis/chapters/1_introduction.tex b/thesis/chapters/1_introduction.tex
index 3231d1e..4ece9a3 100644
--- a/thesis/chapters/1_introduction.tex
+++ b/thesis/chapters/1_introduction.tex
@@ -4,53 +4,31 @@ Artificial intelligence (AI) techniques have recently started enjoying widesprea
 
 However, the successful integration of AI components into production-ready applications demands strong engineering methods in order to achieve robust deployments \cite{serban2020adoption}. That is why it is as important as ever to also focus on the quality and robustness of deployed models and software. For instance, the lack of a proper overview of the data transformation steps may lead to suboptimal performance and to introducing unintended biases which may contribute to the ever-increasing negative externality of misused AI \cite{o2016weapons}.
 
-Concerningly, a peculiar tendency seems to be unfolding: even though industry professionals already have access to numerous frameworks for deploying AI correctly and responsibly, case-studies and developer surveys have found that a considerable fraction of deployments do not follow best practices \cite{serban2020adoption,haakman2021ai,amershi2019software,de2019understanding,sculley2015hidden}. Utilising state-of-the-art machine-learning (ML) models has become reasonably simple; applying them properly is as difficult and nuanced as ever. 
+Concerningly, a peculiar tendency seems to be unfolding: even though industry professionals already have access to numerous frameworks for deploying AI correctly and responsibly, case studies and developer surveys have found that a considerable fraction of deployments do not follow best practices \cite{serban2020adoption,haakman2021ai,amershi2019software,de2019understanding,sculley2015hidden}. Utilising state-of-the-art machine-learning (ML) models has become reasonably simple; applying them properly is as difficult and nuanced as ever. 
 
-This thesis sets out to investigate the reasons behind the apparent asymmetry between the adoption of accessible AI libraries and existing reusable solutions for robust AI deployments. It is hypothesised that the primary reason for the underwhelming adoption rate of best practices is the short supply or professionals equally proficient in the domains of both data science and software engineering. Nevertheless, even without their presence, practitioners could rely on frameworks for automated mature deployment processes. However, the barrier of entry for using such existing libraries is too high, especially when compared with the complexity of AI-libraries.
+This thesis sets out to investigate the reasons behind the apparent asymmetry between the adoption of accessible AI libraries and existing reusable solutions for robust AI deployments. It is hypothesised that the primary reason for the underwhelming adoption rate of best practices is the short supply of professionals equally proficient in the domains of both data science and software engineering. Nevertheless, even without their presence, practitioners could rely on frameworks for automated mature deployment processes. However, the barrier of entry for using such existing libraries is too high, especially when compared with the complexity of AI-libraries.
 
-Therefore, a software framework --- called \textit{GreatAI} --- is designed and its design is presented in this thesis. The principal motivation behind the construction of \textit{GreatAI} is to facilitate the responsible and robust deployment of algorithms and models by designing an accessible API in an attempt to overcome the practical drawbacks of other, similar frameworks. Its name stands for its main aim: to assist easily creating \underline{G}eneral \underline{R}obust \underline{E}nd-to-end \underline{A}utomated, and \underline{T}rustworthy AI deployments.
+Therefore, a software framework --- called \textit{GreatAI}\footnote{\href{https://github.com/schmelczer/great-ai}{github.com/schmelczer/great-ai}} --- is designed and its design is presented in this thesis. The principal motivation behind the construction of \textit{GreatAI} is to facilitate the responsible and robust deployment of algorithms and models by designing an accessible API in an attempt to overcome the practical drawbacks of other, similar frameworks. Its name stands for its main aim: to assist easily creating \underline{G}eneral \underline{R}obust \underline{E}nd-to-end \underline{A}utomated, and \underline{T}rustworthy AI deployments.
 
-The utility of \textit{GreatAI} is validated using the principles of design science methodology \cite{wieringa2014design} through iteratively designing its API and implementation along with the text mining pipeline for a commercial product in collaboration with ScoutinScience B.V. The goal of the aforementioned software suite is to evaluate technical transfer opportunities in scientific publications. Subsequently, interviews are conducted with practitioners for validating the generalisability of the design.
+The utility of \textit{GreatAI} is validated using the principles of design science methodology \cite{wieringa2014design} through iteratively designing its API and implementation in a case study concerning the text mining pipeline for a commercial product in collaboration with ScoutinScience B.V.\footnote{\href{https://scoutinscience.com/}{scoutinscience.com}} The goal of the aforementioned software suite is to evaluate technical transfer opportunities in scientific publications. Subsequently, interviews are conducted with practitioners for validating the generalisability of the design.
 
 \section{Research questions}
 
 I hypothesise that facilitating the adoption of AI deployment best practices is viable by finding less complex framework designs which are easier to adopt in order to decrease the negative externality of misused AI. This paper is set out to investigate this hypothesis by answering the following research questions. 
 
 \begin{rqlist}
-  \item Does the complexity of AI deployment frameworks hinder industrial projects?
-  \item What is an effective way of decreasing the complexity of existing frameworks?
-  \item Does \textit{GreatAI}'s design improve the efficiency of working with AI while also introducing best practices?
-  \item Can the design of \textit{GreatAI} decrease the barrier of entry for applying best practices in other contexts?
+  \item To what extent does the complexity of AI deployment frameworks hinder industrial applications?
+  \item What API design techniques can be effectively applied in order to decrease the complexity of correctly deploying AI services?
+  \item To what extent can \textit{GreatAI} automatically implement AI deployment best practices?
+  \item How adequate is the design of \textit{GreatAI} for helping to apply best practices in other contexts?
 \end{rqlist}
 
-In this case, complexity is used to refer to the difficulty faced by professionals (data scientists and software engineers alike) when integrating libraries with their solutions. This could also be described as the barrier of entry or steepness of the learning curve. If the aforementioned hypothesis is correct, the adoption of best practices can be efficiently increased by decreasing this complexity.
+In this case, complexity refers to the difficulty faced by professionals (data scientists and software engineers alike) when integrating libraries with their solutions. This could also be described as the barrier of entry or steepness of the learning curve. If the aforementioned hypothesis is correct, the adoption of best practices can be efficiently increased by decreasing this complexity.
 
 AI deployment best practices entail the technical steps that ought to be taken in order to achieve robust, end-to-end, automated, and trustworthy deployments. These are detailed in Section \ref{section:requirements}.
 
-The existence question regarding the problem itself (\textbf{RQ1}) is answered by reviewing the literature of the more than 30 published case-studies. \textbf{RQ2} and \textbf{RQ3} are closely connected, the design and evaluation phases utilised to answer them follow an iterative process. They are examined in Chapter \ref{chapter:design} and Chapter \ref{chapter:case} respectively. The final evaluation step is to ascertain the capability of the framework design to generalise beyond a single subdomain and problem context. This question, \textbf{RQ4}, is investigated through interviews with industry professionals in Chapter \ref{chapter:interviews}.
-
-\section{Requirements} \label{section:requirements}
-
-The best practices (which will be referenced throughout the thesis) with which the \textit{GreatAI} design is concerned are a subset of those compiled by Serban et al. \cite{serban2020adoption}. The core requirements --- sets of covered best practices --- for a software solution that has the potential of improving our problem context are presented in the following along with some explanation and clarification of each of them.
-
-\paragraph{General} Albeit not explicitly in the list of best practices, compatibility is vital in encouraging adoption. Large projects oftentimes end up depending on numerous packages, each of which may impose some restrictions on the code: since these all have to be satisfied simultaneously, this can result in severe constraints on the application. 
-
-The open-source scene of data-related libraries is vibrant. To take the example of data validation, there are at least 4 popular choices which offer varying but similar features: \href{https://github.com/SeldonIO/alibi-detect}{Alibi detect}, \href{https://github.com/PAIR-code/facets}{Facets}, \href{https://github.com/great-expectations/great_expectations}{Great Expectations}, and Data Linter \cite{hynes2017data}. The responsibility of choosing the most fitting solution falls on the user, thus, they should not be limited in this by \textit{GreatAI}.
-
-The programming language (PL) of the library should be its only non-general property. Fortunately, the de facto PL for data science is Python, hence, implementing the library in it should not significantly limit its applicability.
-
-\paragraph{Robustness} in software development can be achieved by preparing the application to gracefully handle errors, even unexpected ones \cite{bishop1998robust}. Errors can and will happen in practice: storing and investigating what has led to them is required to prevent future ones. In the case of ML, errors might not be as obvious to detect as in more traditional applications (see the above mentioned data validators). Even if a single feature's value falls outside the expected distribution, unexpected results can happen. In cases where this might lead to real-world repercussions, extra care has to be taken to construct as many safe-guards as feasible. \textit{GreatAI} should support its clients in doing so.
-
-\paragraph{End-to-end} In this case, it refers to end-to-end feedback. That is, feedback should be gathered on the real-world performance of the system, and this should be taken into account when designing/training the next iteration of the model. Static datasets may fail to capture the changing nature of real-life and can become outdated if they are not revised continuously. A well packaged deployment should make it trivial to integrate new training data.
-
-\paragraph{Automated} The available time of data scientists and software engineers is limited and expensive. For this reason, humans should only be involved when their involvement is necessary. Steps in the development process that can be automated without negative consequences must be automated in order to achieve efficient development processes and let the experts focus on the issues that require their attention the most.
-
-\paragraph{Trustworthy} As detailed by the \textit{Ethics guidelines for trustworthy AI}\footnote{\href{https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai}{digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai}}, human oversight, transparency, and accountability are some of the key requirements for trustworthy AI applications. For increasing public acceptance and trust while minimising negative societal impact, trustworthiness is essential.
-
-These requirements were chosen stemming from their general importance and potential to be mostly handled (implemented) by a software framework\footnote{The terms \textit{framework} and \textit{library} are used interchangeably in this work stemming from their vague and often holistic differentiation.}. That is why, these provide an ideal initial direction for tackling the issue. Of course, these do not cover all best practices, for instance, the ones relating to organisational processes fall outside the realm of software engineering.
-
-\newpage
+The existence question regarding the problem itself (\textbf{RQ1}) is answered by reviewing the literature of the more than 30 published case studies. \textbf{RQ2} and \textbf{RQ3} are closely connected, the design and evaluation phases utilised to answer them follow an iterative process. They are examined in Chapter \ref{chapter:design} and Chapter \ref{chapter:case} respectively. The final evaluation step is to ascertain the capability of the framework design to generalise beyond a single subdomain and problem context. This question, \textbf{RQ4}, is investigated through interviews with industry professionals in Chapter \ref{chapter:interviews}.
 
 \section{Structure}
 
-The rest of the thesis is organised as follows: Chapter \ref{chapter:background} approaches the problem and the state-of-the-art from three perspectives: the trends of AI library API design, the experiences gained from practical applications, and a comparison of existing deployment options. Next, the methodology utilised for the subsequent chapters is described in Chapter \ref{chapter:methods}. The design cycle is broken into two chapters, Chapter  \ref{chapter:design} and \ref{chapter:case}. The former clarifies the scope and describes the design principles, while the latter details the specifics of the practical use-case and the framework's interaction with it, and technological contributions of the novel design. The results are further validated by conducting interviews with industry professionals in Chapter \ref{chapter:interviews}. The thesis is concluded in Chapter \ref{chapter:conclusion}.
+The rest of the thesis is organised as follows: Chapter \ref{chapter:background} approaches the problem and the state-of-the-art from three perspectives: the trends of AI library API designs, the experiences gained from practical applications, and a comparison of existing deployment options. Next, the methodology utilised for the subsequent chapters is described in Chapter \ref{chapter:methods}. The design cycle is broken into two chapters, Chapter  \ref{chapter:design} and \ref{chapter:case}. The former clarifies the scope and describes the design principles, while the latter details the specifics of the practical case study, the framework's interaction with it, and technological contributions of the novel design. The results are further validated by conducting interviews with industry professionals in Chapter \ref{chapter:interviews}. The thesis is concluded in Chapter \ref{chapter:conclusion}.
diff --git a/thesis/chapters/2_background.tex b/thesis/chapters/2_background.tex
index 029397b..12f2f55 100644
--- a/thesis/chapters/2_background.tex
+++ b/thesis/chapters/2_background.tex
@@ -6,27 +6,27 @@ In the following, the context of the problem is presented from three perspective
 
 \section{Accessible AI} \label{section:accessible-ai}
 
-Most companies prefer not to develop new models but instead reuse prior ones \cite{bosch2021engineering} and they are able to do so increasingly easier. In recent years, there has been a proliferation of highly accessible AI libraries. For example, let us consider the domain of natural language processing (NLP). There are various options for finding AI solutions that work out of the box: FLAIR \cite{akbik2019flair} and Huggingface's transformers \cite{wolf2019huggingface} let developers access the state-of-the-art models and methods in only a couple of lines of code (in many cases 2 or 3). With the advent of fine-tuneable models such as BERT \cite{devlin2018bert} and its many improved variations, Huggingface enables developers to leverage vast amounts of knowledge learned by any particular model and fine-tune it for their specific use case. The API for this is also extremely accessible.
+Most companies prefer not to develop new models but instead reuse prior ones \cite{bosch2021engineering} and they are able to do so increasingly easier. In recent years, there has been a proliferation of highly accessible AI libraries. For example, let us consider the domain of natural language processing (NLP). There are various options for finding AI solutions that work out of the box: FLAIR \cite{akbik2019flair} and Huggingface's transformers \cite{wolf2019huggingface} let developers access the state-of-the-art models and methods in only a couple of lines of code (in many cases 2 or 3). Using transfer-learning, Huggingface enables developers to leverage vast amounts of knowledge learned by pretrained models (such as BERT \cite{devlin2018bert} and its many improved variations) and fine-tune them for their specific use case. The API exposing this is also extremely accessible.
 
-It is not just these two packages, the list of readily available tools for language processing is vast: SpaCy \cite{srinivasa2018natural}, Gensim \cite{vrehuuvrek2011gensim}, and scikit-learn \cite{pedregosa2011scikit} are other great examples. The situation is similar in all subdomains of artificial intelligence: some domain expertise is --- admittedly --- beneficial but not a hard-requirement. This, combined with the exponentially increasing computing power affordably available to consumers and business alike \cite{sun2019summarizing}, results in AI that is accessible by many.
+It is not just these two packages, the list of readily available tools is vast: SpaCy \cite{srinivasa2018natural}, Gensim \cite{vrehuuvrek2011gensim}, and scikit-learn \cite{pedregosa2011scikit}, XGBoost \cite{Chen_2016} are other great examples. The situation is similar in all subdomains of artificial intelligence: some domain expertise is --- admittedly --- beneficial but not a hard-requirement. This, combined with the exponentially increasing computing power affordably available to consumers and business alike \cite{sun2019summarizing}, results in AI that is accessible by many.
 
 \section{State of the industry} \label{section:industry}
 
 In contrast with this trend, the software landscape around packaging, deploying, and maintaining machine learning (ML) --- and in general --- data-heavy applications paints a different picture. Fortunately, the related issues and their ramifications have been already thoroughly investigated.
 
-When looking at ML code in practice through the lens of technical debt, Sculley et al. \cite{sculley2015hidden} emphasise the repercussions of writing \textit{glue code} between the algorithms and different systems or libraries and define it as an anti-pattern. The consequence of this is the advice against using generic libraries because their rigid API-s may inhibit improvements, cause lock-in, and result in large amounts of glue code. This is a recurring theme in discussions with industry professionals.
+When looking at ML\footnote{The terms AI and ML are often not differentiated and are used as synonyms in practice. For instance, see this study by the FDA \cite{food2019proposed}. ML is a well-defined subdomain of AI, however, most modern AI applications are also ML applications, hence, conflating the two terms may be slightly imprecise but usually not wrong.} code in practice through the lens of technical debt, Sculley et al. \cite{sculley2015hidden} emphasise the repercussions of writing \textit{glue code} between the algorithms and different systems or libraries and define it as an anti-pattern. The consequence of this is the advice against using generic libraries because their rigid API-s may inhibit improvements, cause lock-in, and result in large amounts of glue code. This is a recurring theme in discussions with industry professionals.
 
 Haakman et al. \cite{haakman2021ai} interviewed 17 people at ING which is a well-known fintech company undergoing a digital transformation to embrace AI. They found that the existing tools for ML do not meet the particularities of the field. For instance, a Feature Engineer working in the Data \& Analytics department explained that regular spreadsheets are preferred over existing solutions like MLFlow for keeping track of experiment results. The reason behind this is simplicity. Additionally, multiple other interviewees described the need to self-develop (or highly-customize) dashboards for monitoring deployed models which results in many non-reusable solutions across the company for the same problem. The authors conclude that there is a research gap between the ever-improving SOTA techniques and the challenges of developing real-world ML systems. In short, additional tool support is needed for facilitating the ML lifecycle.
 
 In a case study at Microsoft, Amershi et al. \cite{amershi2019software} interviewed 14 people and surveyed another 551 AI and ML professionals from the company. One of the main concerns surfaced was relating to automation which is a vital cross-cutting concern, especially for testing. At the same time, a human-in-the-loop is still favoured. The survey data pointed out the difficulty posed by integrating AI, especially in the case of less experienced respondents. This was elaborated on by describing the preferences of software engineers as striving for elegant, abstract, modular, and simple systems; in contrast, data tends to be of large volume, context-specific and heterogeneous. Reconciling these inherent differences requires significant effort, nevertheless, Microsoft manages to overcome this with highly sophisticated internal infrastructure.
 
 Using AI is not unique to large companies, in a study conducted with the collaboration of three startups \cite{de2019understanding}, the aim was to fill in the gap of understanding how professionals develop ML systems in small companies. Overall, the results showed they have similar priorities to that of large companies, including an emphasis on the online monitoring of deployed models. However, less structure is present in the development lifecycle, as one interviewee had explained: some steps are left out from time to time because they are forgotten about.
-%The paper does not give detail about the use or in-house development of ML tools.
+
 Similarly, Thiée \cite{thiee2021systematic} describes the slow but ever-growing rate of ML adoption by small and medium-sized enterprises (SMEs). With the caveat that many more of these companies would wish to adopt data-driven approaches but are facing new challenges stemming from the domain's complexity.
 
-Serban et al. \cite{serban2020adoption,serban2021practices} describe the results of their global surveys aiming to ascertain the SOTA in how teams develop, deploy, and maintain ML systems. In \cite{serban2020adoption}, they compiled a set of 29 actionable best practices. These were analysed and validated with a survey of 313 participants to discover the adoption rate and relative importance of each best practice. For example, they determined the most important best practice to be \textit{logging production prediction traces}, however, the adoption was measured to be below 40\%. In more than three quarters of the cases, newcomers to AI reported that they \textit{partially} or \textit{not at all} follow best practices. This tendency decreases with more years of experience, reaching a minimum of just below 40\%. In a similar fashion, Serban et al. in \cite{serban2021practices}, identify another 14 best practices that concern trustworthy AI mainly through data governance. They strive to complement high-level checklists with actionable best practices. Analysing 42 survey responses reveals a familiar pattern. Most best practices have less than 50\% adoption.
+Serban et al. \cite{serban2020adoption,serban2021practices} describe the results of their global surveys aiming to ascertain the SOTA in how teams develop, deploy, and maintain ML systems. In \cite{serban2020adoption}, they compiled a set of 29 actionable best practices. These were analysed and validated with a survey of 313 participants to discover the adoption rate and relative importance of each best practice. For example, they determined the most important best practice to be \textit{logging production prediction traces}, however, the adoption was measured to be below 40\%. In more than three quarters of the cases, newcomers to AI reported that they \textit{partially} or \textit{not at all} follow best practices. This tendency decreases with more years of experience, reaching a maximum adoption rate of just above 60\%. In a similar fashion, Serban et al. in \cite{serban2021practices}, identify another 14 best practices that concern trustworthy AI mainly through data governance. They strive to complement high-level checklists with actionable best practices. Analysing 42 survey responses reveals a familiar pattern. Most best practices have less than 50\% adoption.
 
-Finally, Bosch et al. \cite{bosch2021engineering} organise and structure the problem space of AI engineering research based on their 16 primary case-studies. The authors note the increasing and broad adoption of ML in the industry, while also emphasising that \textit{transition from prototype to production-quality deployment} proves to be challenging for many companies. Large amounts of software engineering expertise is required to create additional facilities for the application such as data pipelines, monitoring, and logging. They define \textit{deployment \& compliance} to be one of the four main categories of problems and describe it as highly underestimated and the source of ample struggle.
+Finally, Bosch et al. \cite{bosch2021engineering} organise and structure the problem space of AI engineering research based on their 16 primary case studies. The authors note the increasing and broad adoption of ML in the industry, while also emphasising that \textit{transition from prototype to production-quality deployment} proves to be challenging for many companies. Large amounts of software engineering expertise is required to create additional facilities for the application such as data pipelines, monitoring, and logging. They define \textit{deployment \& compliance} to be one of the four main categories of problems and describe it as highly underestimated and the source of ample struggle.
 
 \section{Existing solutions} \label{section:existing}
 
@@ -38,9 +38,11 @@ IBM's AutoAI \cite{wang2020autoai} promises to provide automation for the entire
 
 SageMaker offers the most comprehensive suite of tools and service; most importantly it has a set of features called \textit{AWS SageMaker MLOps}. This provides easy and/or default implementations for industry best practices described by Serban et al. \cite{serban2020adoption,serban2021practices}. Among others, it promotes the use of CI/CD, model monitoring, tracing, model versioning, storing both data and models on shared infrastructure, numerous collaboration tools, etc. Nonetheless, SageMaker does not enjoy universal adoption as indicated by the survey data. The cause of this may be the lack of self-hosting option and its relatively high prices: many companies prefer on-premise hosting for privacy and financial reasons \cite{bosch2021engineering}. Additionally, vendor lock-in, and possibly --- in the case where it is not already used for the project --- the initial effort required for setting up AWS integration could be possible deterrents.
 
-When it comes to open-source libraries, we can find the MLOps libraries of both TensorFlow and PyTorch: TensorFlow Extended (TFX) \cite{baylor2017tfx} and TorchX\footnote{\href{https://pytorch.org/torchx/latest/}{pytorch.org/torchx/latest}}. TFX comes with a more mature set of features with the caveat that initial time-investment is needed for their setup. The features of TorchX only concern the distributed deployment to a wide range of providers, including Kubernetes, AWS Batch, or Ray. There is no augmentation for the SE4ML best practices. Given the tight coupling between these libraries and their corresponding ML frameworks, they cannot generalise to models\footnote{The Open Neural Network Exchange (\href{https://onnx.ai/}{onnx.ai}) format could be an option for overcoming these incompatibilities, however, a more universal support is needed for seamless integration.} or algorithms of other frameworks and technologies. 
+When it comes to open-source libraries, we can find the MLOps libraries of both TensorFlow and PyTorch: TensorFlow Extended (TFX) \cite{baylor2017tfx} and TorchX\footnote{\href{https://pytorch.org/torchx/latest/}{pytorch.org/torchx/latest}}. TFX comes with a more mature set of features with the caveat that initial time-investment is needed for their setup. The features of TorchX only concern the distributed deployment to a wide range of providers, including Kubernetes (K8s), AWS Batch, or Ray \cite{moritz2018ray}. There is no augmentation for the SE4ML best practices. Given the tight coupling between these libraries and their corresponding ML frameworks, they cannot generalise to models\footnote{The Open Neural Network Exchange (\href{https://onnx.ai/}{onnx.ai}) format could be an option for overcoming these incompatibilities, however, a more universal support is needed for seamless integration.} or algorithms of other frameworks and technologies. 
 
-Open-source platforms also exist such as MLflow and Seldon Core. They both rely on Kubernetes (k8s) to provide their features. MLflow puts more emphasis on the training phase (in deployment, it lacks a feedback loop which is essential to reach many of the best-practices), while Seldon Core focuses on the deployment stage. The latter comes integrated with a powerful explanation engine, Alibi Explain \cite{klaise2021alibi}. It also boasts the most comprehensive suite of features including outlier detection, online model selection (with multi-armed bandit theory), and distributed tracing. In short, it seems to be the ideal candidate for the title of \textit{framework for robust end-to-end AI deployments}. Its only downside is the amount of complexity propagated to its clients: it is built on top of Kubernetes, and relies on Helm, Ambasador/Istio, Prometheus, and Jaeger for its features. Hence, the first step in using it setting up a k8s cluster with all the required components, then when it comes to model deployment, a Kubernetes configuration file has to be created to make use of Seldon's Custom Resource Definition. These are smaller obstacles if the project is already built on top of k8s; however, even then, software engineers with strong cloud and DevOps background are actively required for using Seldon Core.
+Open-source platforms also exist such as MLflow and Seldon Core. They both rely on Kubernetes to provide their features. MLflow puts more emphasis on the training phase (in deployment, it lacks a feedback loop which is essential for reaching many of the best-practices), while Seldon Core focuses on the deployment stage. The latter comes integrated with a powerful explanation engine, Alibi Explain \cite{klaise2021alibi}. It also boasts the most comprehensive suite of features including outlier detection, online model selection (with multi-armed bandit theory), and distributed tracing. In short, it seems to be the ideal candidate for the title of \textit{framework for robust end-to-end AI deployments}. Its only downside is the amount of complexity propagated to its clients: it is built on top of Kubernetes, and relies on Helm, Ambasador/Istio, Prometheus, and Jaeger for its features. Hence, the first step in using it is setting up a K8s cluster with all the required components, then when it comes to model deployment, a Kubernetes configuration file has to be created to make use of Seldon's Custom Resource Definition. These are smaller obstacles if the project is already built on top of K8s; however, even then, software engineers with strong cloud and DevOps background are actively required for using Seldon Core.
+
+Additionally, increasing attention is given to ML deployments in embedded systems both from a theoretical \cite{john2020ai} and practical \cite{prado2020bonseyes} point of view. Prado et al. \cite{prado2020bonseyes} survey the available deployment frameworks and end-to-end solutions including those for embedded devices. They note their inefficiencies that come from the lack of features and too much rigidity. They introduce their framework for embedded AI deployments which can be used out-of-the box but also lets the users easily replace and extend its pipeline with steps to fit their changing needs and advancements of the field. While Meenu et al. \cite{john2020ai} present and compare different architectural choices for large-scale deployments in edge-computing. They also note that: \textit{"...there is a need to consider and adapt well established SE practices which have been ignored or had a very narrow focus in ML literature"}.
 
 \begin{table}
 \centering
@@ -52,29 +54,31 @@ Open-source platforms also exist such as MLflow and Seldon Core. They both rely
 \begin{tabular}{|l|c|c|c|c|c|c|c|}
 \hline
                              &  AutoAI    & Azure ML   & SageMaker  & TFX        & TorchX     & MLflow     & Seldon Core \\ \hline
-Open-source                  &            &            &            & \checkmark & \checkmark & \checkmark & \checkmark  \\ \hline
-Free                         &            &            &            & \checkmark & \checkmark & \checkmark & \checkmark  \\ \hline
-Vendor-agnostic              &            &            &            & \checkmark & \checkmark & \checkmark & \checkmark  \\ \hline
-AI-agnostic                  &            & \checkmark & \checkmark &            &            & \checkmark & \checkmark  \\ \hline
-E2E feedback                 &            & \checkmark & \checkmark &            &            &            & \checkmark  \\ \hline
-Distributed monitoring       &            & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark\textsuperscript{*} & \checkmark  \\ \hline
-Online model selection       & \checkmark & \checkmark & \checkmark &            &            &            & \checkmark  \\ \hline
-Versioning                   & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark  \\ \hline
-Quick setup                  & \checkmark & \checkmark &            &            &            &            &             \\ \hline
-No dependencies (k8s, cloud) &            &            &            &            & \checkmark &            &             \\ \hline
+Open-source\textsuperscript{1}&            &            &            & \checkmark & \checkmark & \checkmark & \checkmark  \\ \hline
+Self-hosted\textsuperscript{1}&            &            &            & \checkmark & \checkmark & \checkmark & \checkmark  \\ \hline
+Vendor-agnostic\textsuperscript{2}&            &            &            & \checkmark & \checkmark & \checkmark & \checkmark  \\ \hline
+AI-agnostic\textsuperscript{2}&            & \checkmark & \checkmark &            &            & \checkmark & \checkmark  \\ \hline
+E2E feedback\textsuperscript{3}&            & \checkmark & \checkmark &            &            &            & \checkmark  \\ \hline
+Distributed monitoring\textsuperscript{3}&            & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark\textsuperscript{*} & \checkmark  \\ \hline
+Online model selection\textsuperscript{3}& \checkmark\textsuperscript{*} & \checkmark & \checkmark &            &            &            & \checkmark  \\ \hline
+Versioning\textsuperscript{3}& \checkmark & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark  \\ \hline
+Quick setup\textsuperscript{4}& \checkmark & \checkmark &            &            &            &            &             \\ \hline
+No DevOps dependencies\textsuperscript{4}&            &            &            &            & \checkmark &            &             \\ \hline
 \end{tabular}}
 \begin{tablenotes}
+\item[1] For privacy and accountability reasons. \cite{bosch2021engineering}
+\item[2] Minimising required glue code. \cite{sculley2015hidden}
+\item[3] Implementing best-practices. \cite{serban2020adoption,serban2021practices}
+\item[4] Easy integration into existing processes. \cite{haakman2021ai,thiee2021systematic}
 \item[*] Only partial support.
 \end{tablenotes}
 \end{threeparttable}
 \end{table}
 
-Table \ref{table:platform-comparison} shows a high-level overview about the general properties, and some of the features relating to the \textit{Deployment} stage of the CRISP-DM model \cite{wirth2000crisp}. It makes it apparent that there is a coexistence of persisting problems and their promised solutions.
-
-Additionally, increasing attention is given to ML deployments in embedded systems both from a theoretical \cite{john2020ai} and practical \cite{prado2020bonseyes} point of view. Prado et al. \cite{prado2020bonseyes} survey the available deployment frameworks and end-to-end solutions including those for embedded devices. They note their inefficiencies that come from the lack of features and too much rigidity. They introduce their framework for embedded AI deployments which can be used out-of-the box but also lets the users easily replace and extend its pipeline with steps to fit their changing needs and advancements of the field. While Meenu et al. \cite{john2020ai} present and compare different architectural choices for large-scale deployments in edge-computing. They also note that: \textit{"...there is a need to consider and adapt well established SE practices which have been ignored or had a very narrow focus in ML literature"}.
+In summary, the problems expressed in Section \ref{section:industry} can be understood when looking at the available solutions. Table \ref{table:platform-comparison} shows a high-level comparison of frameworks along the dimensions in which practitioners reportedly face difficulties in the \textit{Deployment} stage of the CRISP-DM model \cite{wirth2000crisp}.
 
 \section{Summary}
 
-The surveys and case-studies have shown the industry's continuous struggle to evolve their prototypes into robust and responsible production-ready deployments. Simultaneously, platforms aiming to help overcome this challenge already exist but lack widespread adoption. The frequently recurring explanations for not adopting pre-existing solutions surfaced in Section \ref{section:industry} revolve around their complexity and rigidity. These complaints are validated when looking at the available frameworks in Section \ref{section:existing}. While using AI has become more accessible than ever, deploying remains challenging owing to the lack of any \textit{easy-to-use framework for robust end-to-end AI deployments}.
+The surveys and case studies have shown the industry's continuous struggle to evolve their prototypes into robust and responsible production-ready deployments. Simultaneously, platforms aiming to help overcome this challenge already exist but lack widespread adoption. The frequently recurring explanations for not adopting existing solutions surfaced in Section \ref{section:industry} revolve around their complexity and rigidity. These complaints are validated when looking at the available frameworks in Section \ref{section:existing}. While using AI has become more accessible than ever, deploying remains challenging owing to the lack of any \textit{easy-to-use framework for robust end-to-end AI deployments}.
 
-The coexistence of multiple major obstacles along with their promised solutions and the lack of their wide-spread adoption leads us to believe that current frameworks are inadequate for many contexts. There is an unmet need for accessible AI deployment methods. The revolution brought by FLAIR, HuggingFace, and similar libraries for the domain of ML remains unmatched in the domain of AI engineering.
+The coexistence of multiple major obstacles along with their promised solutions and the lack of their wide-spread adoption leads us to believe that current frameworks are inadequate for many contexts. There is an unmet need for accessible AI deployment methods. The revolution brought by FLAIR, HuggingFace, and similar libraries for the domain of ML remains unmatched in the domain of AI Engineering.
diff --git a/thesis/chapters/3_methods.tex b/thesis/chapters/3_methods.tex
index 10bb2ae..71ac611 100644
--- a/thesis/chapters/3_methods.tex
+++ b/thesis/chapters/3_methods.tex
@@ -4,24 +4,24 @@ The chosen methodology for this study is Design Science which emphasises the nee
 
 As for the empirical cycle, the pragmatist approach is taken since the value of this research lies in its utility. Moreover, pragmatism adopts an engineering approach to research \cite{shull2007guide} which is inline with the philosophy of design science. Additionally, as no research method is without flaws, it is imperative to try to compensate their weaknesses by applying multiple methods. Hence, the study also relies on interviews with professionals for validating the design decisions of \textit{GreatAI}.
 
-\section{Problem context}
-
-The problem context is the difficulty in responsibly transitioning (while following best practices) from prototype industrial AI applications to production-ready deployments. With the possible treatment being libraries with high-level API-s and a set of default settings. It is important to note that \textit{GreatAI} is merely a proof-of-concept, and its aim is to serve as the proxy for the design decisions behind it. Through this, the design can be indirectly evaluated. Hopefully, a by-product will be a library that can be successfully applied to this problem context.
-
-The practical cases used for the evaluation are further elaborated in Chapter \ref{chapter:case}. In short, they focus on individual components of a growing commercial platform with the aim of finding tech-transfer opportunities in academic publications. The main input of the system as a whole are individual PDF-files while the output is a list of metrics describing various aspects of the paper, such as interesting sentences, scientific domains, and the scientific contribution. The output also includes a predicted score used for ranking. This ranking is subsequently processed by the business developers of Technology Transfer Offices (TTO-s) of multiple Dutch and German universities who later give feedback on the results.
-
-Overall, this problem context carries the properties of typical industry use-cases: it utilises a wide-range of text mining methods, contains complex interactions between the services, benefits from the integration of end-to-end feedback, and has to provide the clients with a platform that they can rely on in their organisation's core processes. Since the final ranking affects real people, explainability and robustness are also central questions.
-
 \section{Design \& empirical cycles}
 
-The aim of the project can be summarised using the terminology of design science in the following way: 
+The problem context is the difficulty in responsibly transitioning (while following best practices) from prototype industrial AI applications to production-ready deployments. With the possible treatment being libraries with high-level API-s and a set of default settings. It is important to note that \textit{GreatAI} is merely a proof-of-concept, and its aim is to serve as the proxy for the design decisions behind it. Through this, the design can be indirectly evaluated. Hopefully, a by-product will be a library that can be effectively applied to this problem context.
+
+The practical cases used for the evaluation are further elaborated in Chapter \ref{chapter:case}. In short, they focus on individual components of a growing commercial platform\footnote{\href{https://dashboard.scoutinscience.com/}{dashboard.scoutinscience.com}} with the aim of finding tech-transfer opportunities in academic publications. The main input of the system as a whole are PDF-files while the output is a list of metrics describing various aspects of each paper, such as interesting sentences, scientific domains, and the scientific contribution. The output also includes a predicted score used for ranking. This ranking is subsequently processed by the business developers of Technology Transfer Offices (TTO-s) of multiple Dutch and German universities who later give feedback on the results.
+
+Overall, this problem context carries the properties of typical industry use-cases: it utilises a wide-range of natural language processing methods, contains complex interactions between the services, benefits from the integration of end-to-end feedback, and has to provide the clients with a platform that they can rely on in their organisation's core processes. Since the final ranking affects real people, explainability and robustness are also central questions.
+
+The aim of \textit{GreatAI} can be summarised using the terminology of design science in the following way: 
 \textit{Facilitate the easy adoption of AI deployment best practices
 by finding a less complex framework design 
 which is easier to adopt
-to decrease the negative externality of misused AI.}
+in order to decrease the negative externality of misused AI.}
 
-However, before generalising, the design of the framework is iteratively refined using the feedback acquired from applying it in practical contexts which in this case is the research and development of a smaller and a more complex AI component followed by refactoring larger AI-based applications using the finished framework. The treatment is finding a simpler design which still leads to high-quality deployments as defined in Section \ref{section:requirements}. \textbf{RQ2} and \textbf{RQ3} captures this process; for investigating the feedback acquired from iteratively working --- which is the definition of action research --- on the case will be valuable. 
+However, before generalising, the design of the framework is iteratively refined using the feedback acquired from applying it in practical contexts which in this case is the research and development of a smaller and a more complex AI component using the work-in-progress framework. The treatment is finding a simple design (less cognitively straining to use) which still leads to high-quality deployments as defined in Section \ref{section:requirements}. Questions \textbf{RQ2} and \textbf{RQ3} capture this process; for investigating the feedback acquired from iteratively working on the case --- which is the definition of action research --- is of immense valuable. 
 
-To answer how well the design of \textit{GreatAI} can generalise (\textbf{RQ4}), interviews will be conducted from a population of software engineers and data scientists with varying levels of professional background. Since me and my colleagues are likely to have a bias for (or against) the proposed designs, the first step of checking its applicability in other practical contexts is to ask the opinion of non-affiliated fellow practitioners.
+\section{Generalisability}
 
-\textit{GreatAI} might have the potential to bridge the gap between data science and software engineering. Stemming from the bidirectional nature of bridges, we can look at the framework from two perspectives: for professionals closer to the field of data science, it provides an automatic scaffolding of software facilities that are required for deploying, monitoring, and iterating on their models. For software engineers, it highlights the necessary steps required for robust and improvable deployments, while at the same time saves them from the grunt work of implementing these constructs. While most importantly, it serves as a proxy for the design decisions through which they can be tested and evaluated in their practical context.
+To answer how well the design of \textit{GreatAI} can generalise (\textbf{RQ4}), interviews are conducted from a population of software engineers and data scientists with varying levels of professional background. Since me and my colleagues are likely to have a bias for (or against) the proposed designs, the first step of checking its applicability in other practical contexts is to ask the opinion of non-affiliated fellow practitioners.
+
+todo
diff --git a/thesis/chapters/4_design.tex b/thesis/chapters/4_design.tex
index ed16827..7beaaf4 100644
--- a/thesis/chapters/4_design.tex
+++ b/thesis/chapters/4_design.tex
@@ -1,6 +1,6 @@
 \chapter{Designing the framework} \label{chapter:design}
 
-Providing the users with a high-level of abstraction is not unheard of in the domain of practical AI platforms. Many software-as-a-service products offer features for hiding the details of machine learning applications. However --- as we saw in Section \ref{section:existing} --- these tend to abstract away the details of both data science and AI-engineering, overall hindering the development process. The design proposed here aims to simplify only the deployment related concepts.
+Providing users with a high-level of abstraction is not unheard of in the domain of practical AI platforms. Many software-as-a-service products offer features for hiding the details of machine learning applications. However --- as we saw in Section \ref{section:existing} --- these tend to abstract away the details of both data science and AI-engineering, overall hindering the development process. The design proposed here aims to simplify only the deployment related concepts.
 
 \section{Scope} \label{section:scope}
 
@@ -15,7 +15,27 @@ There have been attempts that at least partially address this issue, however, as
     \label{fig:scope}
 \end{figure}
 
-It is interesting to mention that there is a proliferation\footnote{\href{https://xkcd.com/927/}{xkcd.com/927}} of platform/software as a service (PaaS/SaaS) products for deploying AI\footnote{Such as \href{https://mlem.ai/}{MLEM} or any AutoML SaaS platform, for example, \href{https://www.akkio.com/role/software-engineers}{Akkio} as these often have a one-click deployment feature as well.}. At first, these may look promising, however, they tend to only focus on getting code easily deployed in the cloud: AI best practices are not prioritised in this setup. Nevertheless, in many cases, it may be a suitable option to use such a service and these can also complement GreatAI as illustrated in Figure \ref{fig:scope}. First, the prototype is transformed into a GREAT service and materialised as a common software artifact implementing the best practices. Then, it is either deployed using a deployment SaaS, or by using the organisation's existing software deployment setup.
+It is interesting to mention that there is a proliferation\footnote{\href{https://xkcd.com/927/}{xkcd.com/927}} of platform/software as a service (PaaS/SaaS) products for deploying AI\footnote{Such as \href{https://mlem.ai/}{MLEM}, \href{https://streamlit.io/cloud}{Streamlit} or any AutoML SaaS platform, for example, \href{https://www.akkio.com/role/software-engineers}{Akkio} as these often have a one-click deployment feature as well.}. At first, these may look promising, however, they tend to only focus on getting code easily deployed in the cloud: AI best practices are not prioritised in this setup. Nevertheless, in many cases, it may be a suitable option to use such a service and these can also complement GreatAI as illustrated in Figure \ref{fig:scope}. First, the prototype is transformed into a GREAT service and materialised as a common software artifact implementing the best practices. Then, it is either deployed using a deployment SaaS, or by using the organisation's existing software deployment setup.
+
+\section{Requirements} \label{section:requirements}
+
+The best practices (which will be referenced throughout the thesis) with which the \textit{GreatAI} design is concerned are a subset of those compiled by Serban et al. \cite{serban2020adoption}. The core requirements --- sets of covered best practices --- for a software solution that has the potential of improving our problem context are presented in the following along with some explanation and clarification of each of them.
+
+\paragraph{General} Albeit not explicitly in the list of best practices, compatibility is vital in encouraging adoption. Large projects oftentimes end up depending on numerous packages, each of which may impose some restrictions on the code: since these all have to be satisfied simultaneously, this can result in severe constraints on the application. 
+
+The open-source scene of data-related libraries is vibrant. To take the example of data validation, there are at least 4 popular choices which offer varying but similar features: \href{https://github.com/SeldonIO/alibi-detect}{Alibi detect}, \href{https://github.com/PAIR-code/facets}{Facets}, \href{https://github.com/great-expectations/great_expectations}{Great Expectations}, and Data Linter \cite{hynes2017data}. The responsibility of choosing the most fitting solution falls on the user, thus, they should not be limited in this by \textit{GreatAI}.
+
+The programming language (PL) of the library should be its only non-general property. Fortunately, the de facto PL for data science is Python, hence, implementing the library in it should not significantly limit its applicability.
+
+\paragraph{Robustness} in software development can be achieved by preparing the application to gracefully handle errors, even unexpected ones \cite{bishop1998robust}. Errors can and will happen in practice: storing and investigating what has led to them is required to prevent future ones. In the case of ML, errors might not be as obvious to detect as in more traditional applications (see the above mentioned data validators). Even if a single feature's value falls outside the expected distribution, unexpected results can happen. In cases where this might lead to real-world repercussions, extra care has to be taken to construct as many safe-guards as feasible. \textit{GreatAI} should support its clients in doing so.
+
+\paragraph{End-to-end} In this case, it refers to end-to-end feedback. That is, feedback should be gathered on the real-world performance of the system, and this should be taken into account when designing/training the next iteration of the model. Static datasets may fail to capture the changing nature of real-life and can become outdated if they are not revised continuously. A well packaged deployment should make it trivial to integrate new training data.
+
+\paragraph{Automated} The available time of data scientists and software engineers is limited and expensive. For this reason, humans should only be involved when their involvement is necessary. Steps in the development process that can be automated without negative consequences must be automated in order to achieve efficient development processes and let the experts focus on the issues that require their attention the most.
+
+\paragraph{Trustworthy} As detailed by the \textit{Ethics guidelines for trustworthy AI}\footnote{\href{https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai}{digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai}}, human oversight, transparency, and accountability are some of the key requirements for trustworthy AI applications. For increasing public acceptance and trust while minimising negative societal impact, trustworthiness is essential.
+
+These requirements were chosen stemming from their general importance and potential to be mostly handled (implemented) by a software framework\footnote{The terms \textit{framework} and \textit{library} are used interchangeably in this work stemming from their vague and often holistic differentiation.}. That is why, these provide an ideal initial direction for tackling the issue. Of course, these do not cover all best practices, for instance, the ones relating to organisational processes fall outside the realm of software engineering.
 
 \section{Design principles}
 
@@ -45,6 +65,8 @@ This way, the library attempts to notify its user about the existence of these d
 
 \subsection{Documentation}
 
+For structuring the documentation, the diátaxis approach is taken \cite{Procida_Diataxis_documentation_framework} which prescribes dividing documentation into 4 parts along 2 axes: practical-theoretical and passive-active consumption. The four quadrants derived from this are tutorials, how-to guides, reference, and explanation.
+
 Without a doubt, good documentation is a prerequisite for adoption. Documentation comes in multiple forms: modern integrated development environments (IDEs) tend to show a popup of a function's documentation when requested, at the same time a more comprehensive online documentation and example projects are also still expected. But descriptive error messages can be also viewed as documentation. The library should have quality documentation for all categories.
 
 Once again, we might notice two competing interests: the level-of-detail and the length of the documentation. For example, FastAPI\footnote{\href{https://fastapi.tiangolo.com/async/\#concurrent-burgers}{fastapi.tiangolo.com}}, a popular Python web framework, has extensive descriptions and explanations on all topics related to Python's import system, the HTTP protocol, concurrency, deployment, etc. The actual framework's documentation is sprinkled over these very broad topics. This is certainly helpful for beginners to acquire knowledge from a single place. Nevertheless, this high-level of accessibility actually hinders the process of finding the relevant sections (in CDCB, this shows a trade-off between the support of Searching and Comprehension tasks). My opinion is that linking to external resources about the library's domain are welcome, but the documentation must have a single responsibility: describing the library itself.
diff --git a/thesis/chapters/5_case/2-stage.tex b/thesis/chapters/5_case/2-stage.tex
index 5248edd..ca8be64 100644
--- a/thesis/chapters/5_case/2-stage.tex
+++ b/thesis/chapters/5_case/2-stage.tex
@@ -12,6 +12,8 @@ Compared with Section \ref{section:simple-case}, this time around, the toolset o
 
 Automatic text summarisation (ATS) is one of earliest established problems of text analysis and boasts numerous promising results \cite{el2021automatic}. However, our problem requires generating a special type of summary: it must only concern a single aspect (tech-transfer) of the document. Aspect-based text summarisation has also seen some progress over the last decades \cite{berkovsky2008aspect,hayashi2021wikiasp}, but these approaches require concretely defined topics. Unfortunately, \textit{tech-transfer potential} is anything but a clear topic definition.
 
+todo: extractive vs abstractive
+
 Our numerous discussions and interviews with business developers over the last years made it clear that there is no universally agreed on definition for it. At least, all of them agrees that they know it when they see it. Additionally, most of them agree that they can confidently make a decision at the granularity of sentences. This gives rise to an obvious idea: show the experts something that they can annotate. Because the time of experts is valuable, and relevant sentences are few and far between, extra care needs to be taken to improve the ratio of positive examples in the dataset. The research of Iwatsuki Kenichi on formulaic expressions (FE) \cite{iwatsuki2020evaluation,iwatsuki2021extraction,iwatsuki2021communicative,iwatsuki2022extraction} provides a promising direction to do so. 
 
 A formulaic expression is a phrase with zero or more slots that expresses a certain intent. In the context of scientific texts, an example\footnote{Taken from the ground-truth data at \href{https://github.com/Alab-NII/FECFevalDataset/blob/master/human_evaluation/background.tsv}{github.com/Alab-NII/FECFevalDataset}} could be: \texttt{it was not until * that}. The asterisk can be substituted with multiple terms and the intention of this expression is (likely) to describe the \textit{History of the related topics}. Iwatsuki et al. identified a set of 39 intentions, compiled a manually labelled dataset \cite{iwatsuki2020evaluation}, and developed multiple approaches for automatically extracting and classifying formulaic expressions in large corpora \cite{iwatsuki2021communicative,iwatsuki2022extraction}.
@@ -20,7 +22,7 @@ A formulaic expression is a phrase with zero or more slots that expresses a cert
 
 In the following, we explore a 2-stage retrieval approach \cite{schutze2008introduction} commonly used in the field of information retrieval. The first stage is expected to filter out sentences that are certainly not relevant from a technology-transfer perspective using Iwatsuki's formulaic expression intention labels. Subsequently, the second stage utilises a fine-tuned SciBERT model to rank the remaining sentence based on a model learned from expert annotations.
 
-This approach has multiple shortcoming, for the first stage, we must assume the independence of sentences and that the FE intentions are strongly correlated with the sought after aspect. Additionally, the reranking only considers the individual relevance of the sentences instead of the overall relevance (utility) of the summary. It is expected, that stemming from the length of the documents and the sparseness of the selected sentences, that any combination of them is likely to have low redundancy.
+This approach has multiple shortcomings, for the first stage, we must assume the independence of sentences and that the FE intentions are strongly correlated with the sought after aspect. Additionally, the reranking only considers the individual relevance of the sentences instead of the overall relevance (utility) of the summary. It is expected, that stemming from the length of the documents and the sparseness of the selected sentences, that any combination of them is likely to have low redundancy.
 
 TODO
 
diff --git a/thesis/chapters/5_case/features.tex b/thesis/chapters/5_case/features.tex
index 12b0c23..a9fad45 100644
--- a/thesis/chapters/5_case/features.tex
+++ b/thesis/chapters/5_case/features.tex
@@ -1,31 +1,24 @@
 \section{Bridging \textbf{the gap} with GreatAI}
 
-This section briefly explores how the problems raised can be solved using GreatAI, and the API it provides to best fit the needs of its users. We first focus on the aspects of data, then, the automated wrapping of service, lastly we discuss the utility of helper functions.
+This section briefly explores how the problems raised can be solved using GreatAI, and the API it provides to best fit the needs of its users. We first focus on the aspects of data, then, the automated wrapping of services, lastly we discuss the utility of helper functions.
 
-First, let us revisit the scope. As concluded in Section \ref{section:scope}, GreatAI should ease the \textit{transition} step between prototypes and production-ready deployments. However, this leaves open the question of what constitutes to this step? There are cross-cutting concerns such as the feature extraction code: for example, feature extraction is implemented and used in the training phase but it is also deployed alongside the model. The robustness criterion has to be met by this procedure after deployment even though its implementation is only in focus at the earlier stage of the project. Since having an untested function deployed into production can have severe repercussions, I believe, assuring its correctness lies within the scope of GreatAI.
+Firstly, let us revisit the scope. As concluded in Section \ref{section:scope}, GreatAI should ease the \textit{transition} step between prototypes and production-ready deployments. However, this leaves open the question of what constitutes to this step? There are cross-cutting concerns such as the feature extraction code: for example, feature extraction is implemented and used in the training phase but it is also deployed alongside the model. The robustness criterion has to be met by this procedure after deployment even though its implementation is only in focus at the earlier stage of the project. Since having an untested function deployed into production can have severe repercussions, I believe, assuring its correctness lies within the scope of GreatAI.
 
 \subsection{Data}
 
-There are two kinds of data storage need we need to address: training data and trained models. Because our code is probably already tracked under Git (and likely synced with GitHub), using the Git Large File Storage (LFS)\footnote{\href{https://git-lfs.github.com/}{https://git-lfs.github.com/}} might seem intriguing. However, it is a paid (and surprisingly expensive) service of GitHub especially when we factor in the expected sizes of the models and train data with the fact that the only way remove files counting towards our quota is to \href{https://docs.github.com/en/repositories/working-with-files/managing-large-files/removing-files-from-git-large-file-storage#git-lfs-objects-in-your-repository}{recreate the repository}.
+There are two kinds of data storage needs we need to address: training data and trained models. Because our code is probably already tracked under Git (and likely synced with GitHub), using the Git Large File Storage (LFS)\footnote{\href{https://git-lfs.github.com/}{https://git-lfs.github.com/}} might seem intriguing. However, it is a paid (and surprisingly expensive) service of GitHub especially when we factor in the expected sizes of the models and training data with the fact that the only way remove files counting towards our quota is to \href{https://docs.github.com/en/repositories/working-with-files/managing-large-files/removing-files-from-git-large-file-storage#git-lfs-objects-in-your-repository}{delete the repository}.
 
 The Data Version Control (DVC)\footnote{\href{https://dvc.org/}{https://dvc.org/}} open-source project provides a nearly perfect solution. It comes with a command-line interface (CLI) inspired by git's, and it can be integrated with several backend storage servers. Its only downside is of course that it is one more tool that increases the complexity of the project and the initial setup time. If this is an acceptable price to pay, then I personally recommend opting for DVC. Nevertheless, if this may prohibit a team from properly handling data according to the best practices, I present a simpler solution in the following. 
 
 The complexity of an API can be decreased by relying on its users preexisting knowledge. Therefore, we can reuse familiar API-s, such as the \texttt{open()} method from Python. A method is proposed which provides the same interface, however, the backing storage for it is a mixture of local disk space, S3-compatible storage, MongoDB, or any other storage backend. It provides a superset of \texttt{open()}'s interface; the same parameters can be used with it.
 
-Easing development isn't just about automating everything but also making the code easy to change (which is the \textit{Viscosity} dimension of CDCB). Going from opening a local file on the disk with the built-in open method, to opening  a file from S3 is as easy as changing \texttt{with open('file.txt', 'w' as f: ...} to \texttt{with LargeFileS3('file.txt', 'w' as f: ...}. In the case of the latter, an additional \texttt{version} keyword argument can also be given to lock ourselves in using as certain version which is very much desired in the case of models.
+Easing development isn't just about automating everything but also making the code easy to change (which is the \textit{Viscosity} dimension of CDCB). Going from opening a local file on the disk with the built-in open method, to opening  a file from S3 is as easy as changing \texttt{with open('file.txt', 'w') as f: ...} to \texttt{with LargeFileS3('file.txt', 'w') as f: ...}. In the case of the latter, an additional \texttt{version} keyword argument can also be given to lock ourselves in using a certain version which is very much desired in the case of models.
 
 The obstacles coming from the intertwined nature of different models is widely recognised \cite{sculley2015hidden,haakman2021ai,amershi2019software}. This can lead to non-monotonic error propagation, meaning that improvements in one part of the system might decrease the overall system quality \cite{amershi2019software}. The importance of schema versioning in an environment of rapidly changing models and transformations is highlighted and solved for a specific use-case in \cite{van2017versioning}.
 
-The expected features: progress bar, caching, automatically purging the cache, automatically deleting old remote version if requested are all present and come with recommended --- but easy to see and change --- configuration.
+The expected features: progress bar, caching, garbage collecting the cache, automatically deleting old remote version if requested are all present and come with recommended --- but easy to see and change --- configuration.
 
-\subsection{Utilities}
-
-
-utilities: clean, language, parallel map \textit{Enable Parallel Training Experiments}
-
-traces
-
-\textit{Deployment approach}
+\subsection{Deployment approach}
 
 % Should the order of the decorators matter? all except in one case, they're written in a way that the order doesn't matter even with the original semantics of decorators. In that one case, it cannot be written in that way. Instead of correcting a user's error, there's a mechanism looking for this error and the user is notified. Guessing the unspecified is cool, but correcting the wrong is not
 
@@ -38,3 +31,13 @@ to do
 
 % Argumetn/parameter names were confusing 
 % offlinemode -> cacheonly mode 
+
+\subsection{Utilities}
+
+It is easy to notice multiple recurring tasks when it comes to processing text. Cleaning it from various extraction artifacts and normalising characters is one of the most common. But splitting sentences, classifying its language, robustly lemmatizing are also surprisingly common tasks. Because having reusable and tested feature extraction code covers two best practices, it seems straightforward that a utility module could be created for this which can also be extensively tested by means of unit testing.
+
+This is exactly the motivation behind \texttt{great\_ai.utilities}. Extra care has to be taken not to overfit these utilities on the cases considered in this chapter; I believe, these are versatile enough to be helpful in many text-related context. A conclusive answer to this assumption will be found during the interviews.
+
+Implementing the unit tests uncovered multiple edge cases and even runtime errors, hence, the value in following the \textit{Test all Feature Extraction Code} best practice is cannot be doubted. There is one more best practice that should be partially covered here, especially, because it is useful both during batch inference, but also at training/feature extraction time:  \textit{Enable Parallel Training Experiments}. 
+
+A function called \texttt{parallel\_map()} is implemented which closely mimicks the API of the built-in Python function: \texttt{map}. And it exemplifies how even a close to trivial function is able to improve the DX by magnitudes. Rooted in the global interpreter lock (GIL)\footnote{\href{https://wiki.python.org/moin/GlobalInterpreterLock}{wiki.python.org/moin/GlobalInterpreterLock}} of CPython, in almost all cases, multi-threading does not lead to higher performance of CPU-bound tasks. For this purpose, multiprocessing has to be used. Fortunately, the built-in \texttt{multiprocessing} library has a great API, however, it still takes about a dozen lines to do a parallel mapping task with a progressbar. This can deterr people (at least me) from taking advantage of more than just a single CPU core during explorative experimentation. With \texttt{parallel\_map()}, this challenge becomes a single-line, routine task. 
diff --git a/thesis/chapters/5_case/introduction.tex b/thesis/chapters/5_case/introduction.tex
index b743de1..6bd740e 100644
--- a/thesis/chapters/5_case/introduction.tex
+++ b/thesis/chapters/5_case/introduction.tex
@@ -1,11 +1,11 @@
 \chapter{The ScoutinScience platform} \label{chapter:case}
 
-The core product of ScoutinScience B.V. is its platform. The clients are technology-transfer offices of Dutch and German universities, government organisations (e.g.: Wetsus), and corporates (e.g.: Heraeus Group, Ruma Rubber B.V.) who wish to extend the scope of their R\&D activities. ScoutinScience connects to multiple data sources of academic publications and integrates them into a single database. Each new publication is evaluated with a suite of AI components that ultimately determine its technology transfer potential. Other features are also extracted that help the users get a quick overview of the authors, topics, and contributions of a given piece of research.
+The core product of \href{https://scoutinscience.com/}{ScoutinScience B.V.} is its platform. The clients are technology-transfer offices of Dutch and German universities, government organisations (e.g.: Wetsus), and corporates (e.g.: Heraeus Group, Ruma Rubber B.V.) who wish to extend the scope of their R\&D activities. ScoutinScience connects to multiple data sources of academic publications and integrates them into a single database. Each new publication is evaluated with a suite of AI components that ultimately determine its technology transfer potential. Other features are also extracted that help the users get a quick overview of the authors, topics, and contributions of a given piece of research.
 
 Each client organisation gets to see a different filtered view of this database ranked by the predicted probability of technology transfer opportunities being present. The main motivation is to make these business developers' and other professionals work more efficient by showing them which papers have the largest likelihood of being considered interesting by them. 
 
-To achieve this, we have a service-based architecture \cite{kleppmann2017designing} on the backend, apart from the data integration, communication, and business logic, it is made up of services wrapping simpler (phrase-matching, Naive Bayes) and more sophisticated (conditional random fields, transformer) models. As we will soon see, these can also depend on each other, for instance, based on the predicted scientific domain, a different model may be applied for scoring the paper's certain aspects.
+To achieve this, we have a service-based architecture \cite{kleppmann2017designing} on the backend, apart from the data integration, communication, and business logic, it is made up of services wrapping simpler (phrase-matching, Naïve Bayes) and more sophisticated (conditional random fields, transformer) models. As we will soon see, these can also depend on each other, for instance, based on the predicted scientific domain, a different model can be chosen for scoring certain aspects of papers.
 
-I was the first software engineer on the team which has grown considerably in the past two years. While architecting, designing, and integrating more and better models into our software solution, we noticed the same difficulties as described in Chapter \ref{chapter:background}. The gap between prototypes and production-ready services is larger than it seems. It is also larger than it should be. This motivated me to investigate the state-of-the-art and had found that it is insufficient in many cases. Since the ScoutinScience platform is a quite typical example of applying AI in the industry, it will serve as the real-life case, problem context, and testbed for attempting to design a solution which can advance the state-of-the-art.
+I was among the first engineers on the team which has grown considerably in the past two years. While architecting, designing, and integrating more and better models into our software solution, I experienced the same difficulties as described in Chapter \ref{chapter:background}. The gap between prototypes and production-ready services is larger than it seems. It is also larger than it should be. This motivated me to investigate the state-of-the-art and had found that it is insufficient in many cases. Since the ScoutinScience platform is a quite typical example of applying AI in the industry, it will serve as the real-life case, problem context, and testbed for attempting to design a solution which can advance the state-of-the-art.
 
-In this chapter, the process of designing GreatAI is described along with how it fits into real-life use cases. First, a simple experiment is presented which leads to the implementation of a service, then, as the featureset of the library grows and matures, a more complex service is developed. Subsequently, the close to final library version is used to refactor existing ScoutinScinece services in order to further refine the API of GreatAI. Lastly, the final version of the design is presented and qualitatively evaluated to verify how well it satisfies the requirements described in Section \ref{section:requirements}.
+In this chapter, the process of designing \textit{GreatAI} is described along with how it fits into real-life use cases. First, a simple experiment is presented which leads to the implementation of a service, subsequently, as the feature-set of the library grows and matures, a more complex software service is developed. Lastly, the final version of the design is presented and qualitatively evaluated to verify how well it satisfies the requirements described in Section \ref{section:requirements}.
diff --git a/thesis/chapters/5_case/naive-bayes.tex b/thesis/chapters/5_case/naive-bayes.tex
index 32c7e06..7d65a4e 100644
--- a/thesis/chapters/5_case/naive-bayes.tex
+++ b/thesis/chapters/5_case/naive-bayes.tex
@@ -1,10 +1,12 @@
-\section{A simple case} \label{section:simple-case}
+\section{Domain classification with Naïve Bayes} \label{section:simple-case}
 
-Using different models for slight variations of the same problem is quite commonplace in the industry. For instance, UberEats has a vast, hierarchical set of models for every country, region, and city for calculating the estimated time of delivery \cite{li2017scaling}. We have also found, that in order to best process an academic publication, knowing its domain is essential. The reason for this can be (among others) the wildly different vocabularies of different domains. For example, the term \textit{framework} in computer science almost always refers to a software artifact (usually implying high tech-transfer potential), while in every other domain, \textit{framework} is used to describe theoretical models that are less central to practical applications. Of course, it is not merely the meaning of the terms but more importantly, their distribution that varies significantly. Therefore, the topic of this section is to design and develop a domain prediction model for academic papers.
+Using different models for slight variations of the same problem is quite commonplace in the industry. For instance, UberEats has a vast, hierarchical set of models for every country, region, and city for calculating the estimated time of delivery \cite{li2017scaling}. We have also found, that in order to best process an academic publication, knowing its domain is essential. The reason for this can be (among others) the wildly different vocabularies of different domains. For example, the term \textit{framework} in computer science almost always refers to a software artifact (usually implying high tech-transfer potential), while in most other domains, \textit{framework} is used to describe theoretical models that are less central to practical applications. Of course, it is not merely the meaning of the terms but more importantly: their distribution that varies significantly. Therefore, the topic of this section is to design and develop a domain prediction model for academic papers.
 
 \subsection{Background}
 
-Fortunately, this is one of the oldest subjects of text classification. In fact, Maron introduced the Naive Bayes classifier in 1961 for exactly this purpose: classifying documents' subjects. To look at a more recent approach, SciBERT \cite{beltagy2019scibert} --- a BERT \cite{devlin2018bert} model pretrained on academic publications --- was also used for a similar task in which the domains of sentences have to be decided\footnote{\href{https://paperswithcode.com/sota/sentence-classification-on-paper-field}{paperswithcode.com/sota/sentence-classification-on-paper-field}}. It achieved an F1-score of $0.6571$ after being pretrained on the Semantic Scholar Corpus (SSC) \cite{Lo2020S2ORCTS} and finetuned on the train split of the Microsoft Academic Graph (MAG) dataset \cite{wang2019review}\footnote{SciBERT was applied to a preprocessed version of this dataset available at \href{https://github.com/allenai/scibert/tree/master/data/text_classification/mag}{github.com/allenai/scibert/tree/master/data/text\_classification/mag}}.
+Fortunately, this is one of the oldest text classification tasks. In fact, Maron introduced the Naïve Bayes classifier in 1961 \cite{maron1961automatic} for exactly this purpose: classifying documents' subjects. However, it is still an active problem when it comes to academic texts as indicated by Elsevier funded research carried out by Rivest et al. \cite{rivest2021level}. They created a 176-class classification problem for comparing bibliometric and deep-learning approaches but this comparison is made difficult because 44\% of the labels are \textit{assigned suboptimally} in the ground-truth dataset.
+
+Prior work evaluated SciBERT \cite{beltagy2019scibert} --- a BERT \cite{devlin2018bert} model pretrained on academic publications --- on a simpler version of the task in which the domains of sentences\footnote{Sentences are more appropriate units for processing due to SciBERT's maximum token length of 512 (which comes from its attention mechanism's quadratic complexity \cite{vaswani2017attention}).} have to be decided\footnote{\href{https://paperswithcode.com/sota/sentence-classification-on-paper-field}{paperswithcode.com/sota/sentence-classification-on-paper-field}}. It achieved an F1-score of $0.6571$ after being pretrained on the Semantic Scholar Corpus (SSC) \cite{Lo2020S2ORCTS} and finetuned on the train split of the Microsoft Academic Graph (MAG) dataset \cite{wang2019review}\footnote{SciBERT was applied to a preprocessed version of this dataset available at \href{https://github.com/allenai/scibert/tree/master/data/text_classification/mag}{github.com/allenai/scibert/tree/master/data/text\_classification/mag}}. To the best of my knowledge, no other published work exists on this sentence-classification task. This may be explained by the lack of practical relevance and contrived nature (uniform label distribution) of the task as we will see in the next subsection.
 
 \begin{displayquote}
 \textbf{Design note} After getting familiar with the context, it is time to focus on experimenting and developing our domain prediction service. At the same time, the difficulties encountered should be noted and integrated into GreatAI's design.
@@ -18,25 +20,25 @@ SciBERT's version of the MAG dataset has 84 thousand and 22.3 thousand sentences
 
 \begin{figure}
     \centering
-    \includegraphics[width=\linewidth]{figures/mag-distribution.png}
+    \includegraphics[width=0.5\linewidth]{figures/mag-distribution.png}
     \caption{Class distribution of the MAG  \cite{wang2019review} dataset's 84000 sentences in its \textit{train} split.}
     \label{fig:mag-distribtion}
 \end{figure}
 
-SSC is much larger: it contains over 80 million abstracts. Having more data certainly helps in sampling the term distribution more accurately, the law of diminishing returns apply, especially when using simple models. Therefore, the data will be randomly downsampled to leave us with a more manageable couple of hundred megabytes of abstracts. We can see the distribution of class labels in Figure \ref{fig:ss-distribution}. The dataset is considerably less balanced: \textit{medicine} is by far the most popular field.
+SSC is much larger: it contains over 80 million abstracts. Having more data certainly helps in sampling the term distribution more accurately, nonetheless, the law of diminishing returns apply, especially when using simple models. Therefore, the data will be randomly downsampled to leave us with a more manageable couple of hundreds of megabytes of abstracts. We can see the distribution of class labels in Figure \ref{fig:ss-distribution}. The dataset is considerably less balanced: \textit{medicine} is by far the most voluminous field.
 
 \begin{figure}
     \centering
-    \includegraphics[width=\linewidth]{figures/ss-distribution.png}
-    \caption{Label distribution of the Semantic Scholar dataset \cite{Lo2020S2ORCTS}. The \textit{variable} refers to the position of the domain in the list of domains assigned to a paper.}
+    \includegraphics[width=0.8\linewidth]{figures/ss-distribution.png}
+    \caption{Label distribution of the Semantic Scholar dataset \cite{Lo2020S2ORCTS}. Each publication may be assigned at most 3 domains.}
     \label{fig:ss-distribution}
 \end{figure}
 
 \begin{displayquote}
-\textbf{Where should we store this data?} "On my machine" seems like an easy answer. However, if we have a team working with the data or it has intrinsic value, it must be stored in an easy-to-access, potentially redundant way. Serban et al. \cite{serban2020adoption} expressed this need in the following best practice: \textit{Make Data Sets Available on Shared Infrastructure (private or public)}. Meanwhile, wherever data is stored, it should also be versioned to satisfy the next best practice: \textit{Use Versioning for Data, Model, Configurations and Training Scripts}.
+\textbf{Where should we store this data?} ``On my machine'' seems like an easy answer. However, if we have a team working with the data or it has intrinsic value, it must be stored in an easy-to-access, potentially redundant way. Serban et al. \cite{serban2020adoption} expressed this need in the following best practice: \textit{Make Data Sets Available on Shared Infrastructure (private or public)}. Meanwhile, wherever data is stored, it should also be versioned to satisfy the next best practice: \textit{Use Versioning for Data, Model, Configurations and Training Scripts}.
 \end{displayquote}
 
-MAG needs no further preprocessing if we aim to match SciBERT's setup \cite{beltagy2019scibert}. But since SSC contains a heap of metadata, the relevant parts have to be extracted and preprocessed. In this case, these are the the concatenation of the abstract's text, paper's title and the journal's name along with the paper's domains (there can be multiple domains for a single paper, it is a mulitlabel classification task). Lastly, the non-English entries are discarded because we only expect to process papers in  English.
+MAG needs no further preprocessing if we aim to match SciBERT's setup \cite{beltagy2019scibert}. But since SSC contains a heap of metadata, the relevant parts have to be extracted and preprocessed. In this case, these are the concatenation of the abstract's text, paper's title and the journal's name along with the paper's domains (there can be multiple domains for a single paper, it is a multi-label classification task). Lastly, the non-English entries are discarded because we only expect to process papers in  English.
 
 \begin{displayquote}
 \textbf{How should we preprocess the data?} These simple processing steps (filter, map, project) are almost always present in the data science life-cycle. For example, cleaning the input text from various HTML, OCR, PDF, or \LaTeX \hskip 0.12cm extraction artifacts is almost always necessary for text analysis. This is captured in the AI best-practices collection under the following category: \textit{Write Reusable Scripts for Data Cleaning and Merging}. Also, the best practice of \textit{Test all Feature Extraction Code} is somewhat applicable: the applied processing steps must not introduce unwanted artifacts.
@@ -44,9 +46,11 @@ MAG needs no further preprocessing if we aim to match SciBERT's setup \cite{belt
 
 \subsection{Methods}
 
-Since the aim is to classify papers to allow the ScoutinScience platform to select models which have been trained on a matching vocabulary (and domain), it seems reasonable that only considering the distribution (frequencies) of individual terms may be sufficient. To test this hypothesis, a unigram language model (Multinomial Naive Bayes) is constructed and its accuracy is compared with SciBERT's. The former definitely aligns with the advice to \textit{Use The Most Efficient Models}.
+Our aims are twofold: (1) to evaluate a sentence classification model on MAG and compare it with prior art; and (2) to retrain and apply this model for classifying publication metadata (including abstracts). This would allow the ScoutinScience platform to select an appropriate processing pipeline which has been trained on a matching vocabulary (and domain) for each publication. 
 
-Using the MNB implementation of scikit-learn \cite{pedregosa2011scikit}, it only took a couple of lines to create, hyperparameter-optimise, and test a text classifier. Including data loading and visualisations, it takes 71 LOC to be more precise. \footnote{The code is available at \href{https://github.com/ScoutinScience/great-ai/blob/main/examples/simple-mag/train.ipynb}{github.com/ScoutinScience/great-ai/blob/main/examples/simple-mag/train.ipynb}} This further proves relatively how simple it is to apply existing algorithms. The code can be considered for satisfying the \textit{Automate Hyper-Parameter Optimisation} best-practice, since it also implements an automated hyperparameter sweep. 
+It seems reasonable that only considering the distribution (frequencies) of individual terms may be sufficient. To test this hypothesis, a unigram language model (Multinomial Naïve Bayes) is constructed and its accuracy is compared with SciBERT's. The former definitely aligns with the advice to \textit{Use The Most Efficient Models}\footnote{\href{https://se-ml.github.io/best_practices/02-efficient-models/}{se-ml.github.io/best\_practices/02-efficient-models}}.
+
+Using the MNB implementation of scikit-learn \cite{pedregosa2011scikit}, it only took a couple of lines to create, hyperparameter-optimise, and test a text classifier. Including data loading and visualisations, it takes 71 lines of code (LOC) to be more precise. \footnote{The code is available at \href{https://great-ai.scoutinscience.com/tutorial/}{great-ai.scoutinscience.com/tutorial}.} This further proves relatively how simple it is to use standard packages. The code can be considered for satisfying the \textit{Automate Hyper-Parameter Optimisation} best-practice, since it also implements an automated hyperparameter sweep. 
 
 The sentences are tokenised into words and vectorised with TF-IDF (with logarithmic term frequency) \cite{buckley1985implementation}, the hyperparameters found via 3-fold cross-validation on the \textit{train} split lead to filtering out tokens which occur in fewer than 5 documents or more than 5\% of the documents.
 
@@ -58,25 +62,25 @@ The sentences are tokenised into words and vectorised with TF-IDF (with logarith
 
 \begin{figure}
     \centering
-    \includegraphics[width=0.8\linewidth]{figures/confusion-matrix.png}
-    \caption{Confusion matrix of a Naive Bayes classifier on the MAG dataset's sentences. The matrix is normalised column-wise. Notice, how most mistakes happen between semantically similar classes, for instance: \textit{politics} -- \textit{sociology} or \textit{business} -- \textit{economics}.}
+    \includegraphics[width=0.8\linewidth]{figures/mag-confusion.png}
+    \caption{Confusion matrix of a Naïve Bayes classifier on the MAG dataset's sentences. The matrix is normalised column-wise. Notice, how most mistakes happen between semantically similar classes, for instance: \textit{politics} -- \textit{sociology} or \textit{business} -- \textit{economics}.}
     \label{fig:mag-confusion}
 \end{figure}
 
 \begin{figure}
     \centering
     \includegraphics[width=\linewidth]{figures/ss-confusion.png}
-    \caption{Confusion matrix of a Naive Bayes classifier on the SSC dataset's sentences. The matrix is normalised column-wise. Notice, how most mistakes happen between semantically similar classes, for instance:  \textit{philosohpy} -- \textit{sociology} or \textit{history} -- \textit{art}.}
+    \caption{Confusion matrix of a Naïve Bayes classifier on the SSC dataset's sentences. The matrix is normalised column-wise. Notice, how most mistakes happen between semantically similar classes, for instance:  \textit{philosohpy} -- \textit{sociology} or \textit{history} -- \textit{art}.}
     \label{fig:ss-confusion}
 \end{figure}
 
-When this model is applied to the \textit{test} split of MAG, we get the confusion matrix of Figure \ref{fig:mag-confusion}. This Naive Bayes classifier achieves a whopping $0.6795$ F1-score. This is $3.4\%$ better than SciBERT's on the same dataset. Thus, it seems, MNB clearly outperforms SciBERT for this particular use-case: it is not only more accurate, its model is magnitudes smaller, while it is also considerably faster to train (or finetune in the case of SciBERT) and use.
+When this model is applied to the \textit{test} split of MAG, we get the confusion matrix of Figure \ref{fig:mag-confusion}. This Naïve Bayes classifier achieves a whopping $0.6795$ F1-score. This is $2.3\%$ more than SciBERT's on the same dataset. Thus, it seems, MNB clearly outperforms SciBERT for this particular use-case: it is not only more accurate, its model is magnitudes smaller, while it is also considerably faster to train (or finetune in the case of SciBERT) and use (its running time is in the order of milliseconds per publication). It also has no upper-limit on the input length. Thus, this experiment validates the choice of picking MNB for the task over SciBERT.
 
-It is, of course, not entirely surprising that the sophisticated transformer architecture of SciBERT is not necessary for a plain task like this. Apart from phrases, the relation between separate words of a sentence do not carry nearly as much discriminative power as the identity of the terms\footnote{On a similar note, the independence assumption of Naive Bayes is often less wrong than it might seem \cite{hand2001idiot}.}, hence there is little reason for using an attention mechanism. The fact that SciBERT even works in any way on this task is already a testament to its general applicability. Nevertheless, this short experiment has proved that we can safely opt for using MNB for production.
+It is, of course, not entirely surprising that the sophisticated transformer architecture of SciBERT is not necessary for a plain task like this. Apart from phrases, the relation between separate words of a sentence do not carry nearly as much discriminative power as the identity of the terms\footnote{On a similar note, the independence assumption of Naïve Bayes is often less wrong than it might seem \cite{hand2001idiot}.}, hence there is little reason for using an attention mechanism. The fact that SciBERT even works in any way on this task is already a testament to its general applicability. Nevertheless, this short experiment has proved that we can safely opt for using MNB for production.
 
-Since Multinomial Naive Bayes is best at returning a single label and SSC is has multiple labels per datapoint: for evaluation purposes, it is checked whether the returned label is contained in the labels of the ground truth. On this dataset, MNB achieves a significantly lower macro-average F1-score which is 0.49. The weighted-average F1 is 0.61 and the overall accuracy is 62\%. The large difference between the macro and weighted averages come from the unbalanced distribution of the labels, better performance could be achieved by uniformly sampling from each class.
+Since Multinomial Naïve Bayes is best at returning a single label and SSC has multiple labels per datapoint: for evaluation purposes, it is checked whether the returned label is contained in the labels of the ground truth. On this dataset, MNB achieves lower macro-average F1-score which is 0.59.\footnote{The code for this is available at \href{https://great-ai.scoutinscience.com/examples/simple/deploy}{great-ai.scoutinscience.com/examples/simple/deploy}.} The weighted-average F1 is 0.70 and the overall accuracy is also 70\%. The substantial difference between the macro and weighted averages come from the unbalanced distribution of the labels.
 
-The lower F1-score is not surprising because there are more than twice as many classes in this dataset,  Additionally, the mistakes made are defendable when we look at Figure \ref{fig:ss-confusion}: most of them are between close or related classes.
+The lower F1-score is not surprising because there are more than twice as many classes in this dataset,  Additionally, the mistakes made are defensible when we look at Figure \ref{fig:ss-confusion}: most of them are between close or related classes.
 
 \begin{displayquote}
 This is the usual point where papers conclude: a proof-of-concept/prototype has been built and its performance demonstrated, measured --- and usually --- explained. Nonetheless, in an industrial setting, our problem is far from being solved: it has yet to be deployed.
@@ -87,7 +91,7 @@ This is the usual point where papers conclude: a proof-of-concept/prototype has
 First, an inference function needs to be written that can take an input on the fly and calculate a corresponding prediction. Since we aim to follow the best practices, namely: \textit{Explain Results and Decisions to Users} and \textit{Employ Interpretable Models When Possible}, giving an explanation of the results is expected. Fortunately, with our simple model it's easy to determine the most influential weights, thus, words. The last deployment step may be to provide access to our model for others.
 
 \begin{displayquote}
-\textbf{How do we provide an interface for the inference function?} We either have an offline or online inference workflow (or both). For the former, we have to provide a way to use it in batch processing; a simple Python function may be adequate for this purpose, though, allowing it to be easily (or automatically) parallelised would make its consumers' DX better. If it is an online-workflow, we must have a service running continuously and accepting input at any time. This can be achieved by a remote procedure call (RPC) interface, or more commonly, a web API. Developers usually refer to these as REST API-s, sometimes, they even follow the conventions of REST. Either ways, we must develop a wrapper over the service in order to make it available for other internal/external consumers.
+\textbf{How do we provide an interface for the inference function?} We either have an offline or online inference workflow (or both). For the former, we have to provide a way to use it in batch processing; a simple Python function may be adequate for this purpose, though, allowing it to be easily (or automatically) parallelised would make its consumers' DX better. If it is an online-workflow, we must have a service running continuously and accepting input at any time. This can be achieved by a remote procedure call (RPC) interface, or more commonly, a web API. Developers usually refer to these as REST API-s, sometimes, they even follow the conventions of REST. Either way, we must develop a wrapper over the service in order to make it available for other internal/external consumers.
 \end{displayquote}
 
 According to the research on the adoption of best practices, this is where many real-world projects conclude. This happens to be \textbf{the gap}. Believing that solely focusing on the research and experiments is good enough is a fallacy: when following this approach, the deployment step ends up being a rushed attempt of wrapping the \textit{AI} and putting it in the production environment. This is inarguably a deployment. However, it follows very few of the best practices. This can lead to suboptimal real-life performance, lack of accountability, lack of opportunity to improve, and can overall lead to negative societal impact \cite{o2016weapons}.
diff --git a/thesis/chapters/7_conclusion.tex b/thesis/chapters/7_conclusion.tex
index 30580ea..b81ccb7 100644
--- a/thesis/chapters/7_conclusion.tex
+++ b/thesis/chapters/7_conclusion.tex
@@ -2,6 +2,8 @@
 
 % even if you already implemented these solutions by hand, you no longer have to -> you have more time -> you can spend that time implementing more advanced best practices
 
+\textit{GreatAI} might have the potential to bridge the gap between data science and software engineering. Stemming from the bidirectional nature of bridges, we can look at the framework from two perspectives: for professionals closer to the field of data science, it provides an automatic scaffolding of software facilities that are required for deploying, monitoring, and iterating on their models. For software engineers, it highlights the necessary steps required for robust and improvable deployments, while at the same time saves them from the menial work of implementing these constructs. While most importantly, it serves as a proxy for the design decisions through which they can be tested and evaluated in their practical context.
+
 \section{Future work}
 
 \section{Concluding remarks}
diff --git a/thesis/figures/confusion-matrix.png b/thesis/figures/confusion-matrix.png
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diff --git a/thesis/frontpage/frontpage.tex b/thesis/frontpage/frontpage.tex
index cfa350a..97b84f2 100644
--- a/thesis/frontpage/frontpage.tex
+++ b/thesis/frontpage/frontpage.tex
@@ -25,7 +25,7 @@
 
 \begin{tabular}[t]{p{3.5cm}@{\hspace{4mm}\vrule width 1.5pt\hspace{4mm}}l}
 %Logo Leiden University
-\makebox(20,0)[t]{\includegraphics{../figures/UL_PMS-kleur.eps}}
+\makebox(20,0)[t]{\includegraphics{../figures/leiden-logo.eps}}
 &
 \begin{minipage}[t]{12.25cm}
 \begin{Huge}
diff --git a/thesis/llncs.cls b/thesis/llncs.cls
index d49aaa8..b161d00 100644
--- a/thesis/llncs.cls
+++ b/thesis/llncs.cls
@@ -196,6 +196,7 @@
               \fi}
 
 \newcommand\chapter{\clearpage
+                    \setcounter{section}{0}
                     \global\@topnum\z@
                     \@afterindentfalse
                     \secdef\@chapter\@schapter}
@@ -1086,8 +1087,8 @@ to0pt{\kern0.55\wd0\vrule height0.45\ht0\hss}\box0}}}}
       \item[\hskip\labelsep{##3##1}]{##3##2\@thmcounterend\ }}
       }
 
-\renewenvironment{abstract}{%
-      \list{}{\advance\topsep by0.35cm\relax\small
+\renewenvironment{abstract}{
+      \list{}{\advance\topsep by 0.35cm\relax\small
       \leftmargin=1cm
       \labelwidth=\z@
       \listparindent=\z@
@@ -1096,7 +1097,7 @@ to0pt{\kern0.55\wd0\vrule height0.45\ht0\hss}\box0}}}}
       \item[\labelsep\bfseries]
       \section*{Abstract}
 }
-    {\endlist}
+{\endlist}
 
 \newdimen\headlineindent             % dimension for space between
 \headlineindent=1.166cm              % number and text of headings.
diff --git a/thesis/main.tex b/thesis/main.tex
index 71c6a3d..a5186b6 100644
--- a/thesis/main.tex
+++ b/thesis/main.tex
@@ -20,6 +20,7 @@
 \renewcommand{\headrulewidth}{0pt}
 \fancyfoot[C]{\thepage}
 
+% 2-column bibliography
 \makeatletter
 \renewenvironment{thebibliography}[1]
      {\begin{multicols}{2}[\section*{\refname}]%
@@ -72,6 +73,10 @@
  \end{shaded*}
 }
 
+% Section numbering
+\renewcommand\thechapter{\arabic{chapter}}
+\renewcommand\thesection{\thechapter.\arabic{section}}
+\renewcommand\thesubsection{\thesection.\arabic{subsection}}
 
 \begin{document}
 
diff --git a/thesis/ref.bib b/thesis/ref.bib
index b307cf3..77ca2e4 100644
--- a/thesis/ref.bib
+++ b/thesis/ref.bib
@@ -527,3 +527,65 @@
   year={2009},
   publisher={Pearson Education}
 }
+
+@inproceedings{Chen_2016,
+	doi = {10.1145/2939672.2939785},
+	url = {https://doi.org/10.1145\%2F2939672.2939785},
+	year = 2016,
+	month = {aug},
+	publisher = {{ACM}},
+	author = {Tianqi Chen and Carlos Guestrin},
+	title = {{XGBoost}},
+	booktitle = {Proceedings of the 22nd {ACM} {SIGKDD} International Conference on Knowledge Discovery and Data Mining}
+}
+
+@article{food2019proposed,
+  title={Proposed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD)},
+  author={Food and Drug Administration and others},
+  year={2019},
+  publisher={Department of Health and Human Services (United States)}
+}
+
+@inproceedings{moritz2018ray,
+  title={Ray: A distributed framework for emerging $\{$AI$\}$ applications},
+  author={Moritz, Philipp and Nishihara, Robert and Wang, Stephanie and Tumanov, Alexey and Liaw, Richard and Liang, Eric and Elibol, Melih and Yang, Zongheng and Paul, William and Jordan, Michael I and others},
+  booktitle={13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18)},
+  pages={561--577},
+  year={2018}
+}
+
+@article{rivest2021level,
+  title={level classification of scientific publications: A comparison of deep learning, direct citation and bibliographic coupling},
+  author={Rivest, Maxime and Vignola-Gagn{\'e}, Etienne and Archambault, {\'E}ric},
+  journal={PloS one},
+  volume={16},
+  number={5},
+  pages={e0251493},
+  year={2021},
+  publisher={Public Library of Science San Francisco, CA USA}
+}
+
+@article{maron1961automatic,
+  title={Automatic indexing: an experimental inquiry},
+  author={Maron, Melvin Earl},
+  journal={Journal of the ACM (JACM)},
+  volume={8},
+  number={3},
+  pages={404--417},
+  year={1961},
+  publisher={ACM New York, NY, USA}
+}
+
+@article{vaswani2017attention,
+  title={Attention is all you need},
+  author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia},
+  journal={Advances in neural information processing systems},
+  volume={30},
+  year={2017}
+}
+
+@misc{Procida_Diataxis_documentation_framework,
+author = {Procida, Daniele},
+title = {{Diátaxis documentation framework}},
+url = {https://diataxis.fr/}
+}